Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere without the permission of the Author. EFFECT OF METHOD OF TILLAGE ON LOSS OF CARBON FROM SOILS A thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand. Amandeep Singh Ghatohra 2012 i Abstract Soils represent the largest terrestrial carbon (C) pool. Different tillage practices have been shown to result in variable losses of soil C. Among these, No-tillage is regarded as an effective management practice for conserving SOC and reducing soil CO 2 emissions. Overseas research shows that No-tillage practice could reduce CO 2 emissions by approximately 3 Mg CO 2 ha -1 yr-1 . Quantitative information comparing soil CO 2 emissions with No-tillage and conventional tillage is limited in New Zealand. Furthermore, little quantitative information is available on the effect of soil and climatic conditions in modifying these emissions. This Ph.D. study evaluated the potential for Cross Slot ? No-tillage cultivation to reduce CO 2 emissions from cropped soils in New Zealand conditions. A series of preliminary experiments were conducted to establish a suitable chamber method to collect and measure CO 2 emissions from soil. The alkali trap method was selected for use in traditionally cultivated agricultural soils. Another expe riment was conducted to test whether pressure fluctuations caused by wind velocity differentially influence soil CO 2 emissions from conventionally and Cross Slot ? No-tillage cultivated soils. Carbon dioxide emissions from conventionally cultivated soils rapidly equilibrated to the onset of lower (negative) pressure, whereas CO 2 emissions from No-tillage so ils took longer to equilibrate. Experiments on the potential savings of soil C with Cross Slot ? No-tillage cultivation (NT) compared to simulated tillage, measure d in the laboratory showed reduced (between 113 and 393 kg CO 2 -C ha -1 ) CO 2 losses in three out of four soils. This reduction in CO 2 losses was further verified with measurements made for one of the soils at a field site during autumn and summer seasons. Overall the result s of field studies suggest that Cross Slot ? No- tillage cultivation reduced ~3.0 Mg CO 2 ha -1 compared with rotary tillage for combined autumn and summer sowings i.e. two cultivations. A subsequent laboratory incubation study assessed CO 2 loss with different levels of residue addition to the four soils used in the previous laboratory and field experiments. A number of labile C fractions extracted from these soils were measured in an attempt to predict CO 2 losses. These did not show any relationship with the CO 2 respired during the incubation period. It was, therefore, not po ssible to develop a soil test to predict CO 2 losses using these extractions. Modelling laboratory CO 2 respiration data for predicting the CO 2 losses from conventional and No-tillage soils was expl ored using relationships between short-term CO 2 ii respired and total CO 2 loss. The model developed from laboratory incubations was further improved with parameterising the soil temperature and moisture effects. The temperature and moisture sensitive model was used to predict the CO 2 emissions measured during the summer season. The model precisely predicted the amount of C lost from No-tillage soils but the amount predicted for rotary tilled soils was 30 per cent less than the amount of C that was lost in the field. Moreover, the model predicted C loss was higher for the No-tillage soils than the rotary tilled soils which was contradictory to the findings from the field study. Therefore, further work is required as the data obtained during this Ph.D. study was insufficient to provide, or develop a m odel that could be used to predict CO 2 loss from conventional and No-tillage cultivation in New Zealand soils. iii DEDICATION This work is dedicated to my wife Rupinder Kaur who gave me, more than strength, a reason to go on. iv Acknowledgements I would never have been able to finish my dissertation without the guidance of my supervisors, help from laboratory staff, fr iends, and support from my family and wife. I would like to express my deepest gratitude to Professor Dr Surinder Saggar, my chief supervisor for his excellent guidance, caring, patience, tremendous support during my times of hardship and for having faith in me. I am sincerely grateful to my co-supervisor Pr ofessor Dr M. J. Hedley for his valuable scientific inputs and advice throughout the entire study period. I would like to thanks my co-supervisor Dr C. W. Ross for his continuous support and guidance. I am grateful to BNT Carbon Limited and Nufa rm NZ for providing me the financial support during the three years of my research. I am thankful to Douglas Giles for timely help in coordinating tillage operations and soil cores collection at all the sites used in laboratory studies, and for allowing access to his farm to conduct field studies. I am indebted to all the farmers for allowing such ready access to their fields. Many thanks to staff of the Soil and Earth Sciences group especially to Mr Lance Currie, Mr Bob Toes, Mr Ian Furkert, Mr Ross Wallace, Mr Mike Bretherton, Ms Glenys Wallace, Ms Liza Haarhoff, Ms Anne West, Ms Moira Hubbard and Dr Marta Camps for their support and encouragement during my study period. My sincere thanks to Landcare Research for al lowing me to use their research equipment. Thanks are also due to Mr Peter Berben, Ms Suzanne Lambie and Dr Donna Giltrap for their ever helping attitude. My sincere thanks to Peter Bishop, Helen Wa lker, Neha Jha, Tao Wang, Saman Herath and Jeya Kumar for their invaluable support. I owe a big thanks to my dear friend Saleem Bhatti for always being kind, extremely helpful and supportive during my difficult stages of Ph.D. I have no words to thank my father and parents in law for their love, help and encouragement. My heartfelt respect for my dearest mother Late Ms ?Narinder Kaur?, who always loved me, supported me, believed in me and was there for me. Had she been alive she would have been proud. Finally, thanks a ton to a wonderful woman, my wife ? Rupinder Kaur. A person who supports me, loves me, cares for me, no matter what. v Table of Contents Abstract ............................................................................................................................... i Acknowledgements ............................................................................................................. iv Table of Contents ................................................................................................................ v List of Tables ................................................................................................................ ...... x List of Figures ............................................................................................................... ...... xiii List of Plates ................................................................................................................ ....... xv Chapter-1 Introduction ................................................................................................. 1 1.1 Issue ............................................................................................................... 1 1.2 Research Objectives ...................................................................................... 3 1.3 Thesis structure.............................................................................................. 4 1.4 Significance of study ..................................................................................... 6 1.5 References ..................................................................................................... 7 Chapter-2 Review of literature ..................................................................................... 9 2.1 Soil carbon contribution to global warming .................................................. 9 2.2 Common tillage practices .............................................................................. 10 2.2.1 Conventional tillage ......................................................................... 11 2.2.2 Conservation tillage .......................................................................... 11 2.2.3 No-tillage .......................................................................................... 12 2.3 Effect of tillage practices on soil carbon dynamics ....................................... 13 2.3.1 Carbon storage .................................................................................. 13 2.3.2 Carbon dioxide losses ....................................................................... 21 2.3.2.1 Effect of wind speed on CO 2 emissions from soil ................. 22 2.3.3 Carbon fractions ............................................................................... 26 2.4 Factors affecting soil carbon loss .................................................................. 27 2.4.1 Soil temperature ............................................................................... 27 2.4.2 Soil moisture..................................................................................... 29 2.4.3 Soil texture ....................................................................................... 30 2.5 Methods for measuring carbon dioxide fluxes .............................................. 32 2.5.1 Chamber methods ............................................................................. 32 2.5.1.1 Alkali trap approach ................................................................ 32 2.5.1.2 Flux gradient approach ............................................................ 33 2.5.1.3 Closed dynamic chamber approach ......................................... 34 vi 2.5.1.4 Open dynamic chamber approach ........................................... 34 2.5.2 Micrometeorological techniques ...................................................... 35 2.6 Nature of soil organic carbon and separation techniques .............................. 35 2.6.1 Chemical characterisation ................................................................ 36 2.6.2 Chemical fractionation techniques ................................................... 37 2.6.3 Physical characterisation .................................................................. 39 2.6.4 Physical fractionation techniques ..................................................... 40 2.6.4.1 Aggregate fractions ................................................................. 40 2.6.4.2 Particle size fractions............................................................... 41 2.6.4.3 Density fractions...................................................................... 42 2.6.5 Biological characterisation ............................................................... 44 2.6.6 Biological approaches ...................................................................... 44 2.7 Conclusions ................................................................................................... 45 2.8 References ..................................................................................................... 47 Chapter-3 Comparisons of methods to measure carbon dioxide emissions from soils and wind effects on soil carbon dioxide fluxes under controlled laboratory conditions .................................................................................. 76 3.1 Introduction ................................................................................................... 76 3.2 Material and methods .................................................................................... 77 3.2.1 Description of soils ........................................................................... 77 3.2.2 Measurement of CO 2 flux................................................................. 79 3.2.3 Statistical analysis ............................................................................ 81 3.3 Results ........................................................................................................... 81 3.3.1 Low emission study .......................................................................... 81 3.3.2 High emission study ......................................................................... 83 3.4 Discussion ..................................................................................................... 84 3.4.1 Low emission study .......................................................................... 84 3.4.2 High emission study ......................................................................... 86 3.5 Summary and conclusions ............................................................................. 88 3.6 Effect of w ind induced pressure fluctuations on carbon dioxide fluxes from simulated tillage and No-tillage treatments .......................................... 90 3.6.1 Introduction ................................................................................................... 90 3.6.2 Material and methods .................................................................................... 90 3.6.2.1 Soil characteristics ......................................................................... 90 3.6.2.2 Chemical and physical analysis ..................................................... 90 vii 3.6.2.3 Collection of soil cores .................................................................. 91 3.6.2.4 Application of different pressures to soils ..................................... 91 3.6.2.5 Carbon dioxide measurements ...................................................... 92 3.6.2.6 Statistical analysis ......................................................................... 93 3.6.3 Results ........................................................................................................... 93 3.6.4 Discussion ..................................................................................................... 96 3.6.5 Summary and conclusions ............................................................................. 97 3.7 References ..................................................................................................... 98 Chapter-4 Carbon dioxide emissions from soils following rotary? and No-tillage seed bed preparation under field and controlled laboratory conditions ..................................................................................................... 104 4.1 Introduction ................................................................................................... 104 4.2 Material and methods .................................................................................... 105 4.2.1 Laboratory study............................................................................... 105 4.2.1.1 Sites and soils .......................................................................... 105 4.2.1.2 Collection of soil cores and simulation of tillage .................... 106 4.2.1.3 Carbon dioxide measurements ................................................ 106 4.2.1.4 Calculations of CO 2 fluxes for NT cores................................. 107 4.2.1.5 Soil moisture content (SMC) ................................................... 108 4.2.1.6 Cumulative CO 2 emissions ...................................................... 108 4.2.2 Field study ........................................................................................ 109 4.2.2.1 Site and soil ............................................................................. 109 4.2.2.2 Establishment of field experiment ........................................... 109 4.2.2.3 Carbon dioxide measurements ................................................ 110 4.2.2.4 Soil temperature and moisture content .................................... 110 4.2.2.5 Cumulative CO 2 emissions ...................................................... 111 4.2.3 Collection of soil samples for physical and chemical analysis ........ 111 4.2.3.1 Physical analysis...................................................................... 111 4.2.3.2 Chemical analysis .................................................................... 111 4.2.4 Collection of climate data................................................................. 111 4.2.5 Measurements of above- and below-ground residue inputs ............. 112 4.2.6 Statistical analysis ............................................................................ 112 4.3 Results ........................................................................................................... 113 4.3.1 Laboratory study................................................................................ 113 4.3.2 Field study ......................................................................................... 117 viii 4.4 Discussion ..................................................................................................... 125 4.4.1 Laboratory study............................................................................... 125 4.4.2 Field study ........................................................................................ 126 4.4.3 Comparisons of CO 2 emissions during laboratory and field studies ............................................................................................... 128 4.5 Summary and conclusions ............................................................................. 128 4.6 References ..................................................................................................... 130 Chapter-5 Influence of soil type and rates of residue addition on decomposition of residues in soils under controlled laboratory conditions and the relationship of CO2 evolved during the entire incubation period with labile soil C fractions ................................................................................... 134 5.1 Introduction ................................................................................................... 134 5.2 Material and methods .................................................................................... 135 5.2.1 Soils .................................................................................................. 135 5.2.2 Plant material .................................................................................... 136 5.2.3 Decomposition study ........................................................................ 136 5.2.4 Soil fractionation .............................................................................. 137 5.2.4.1 Light and heavy fraction density separation............................ 137 5.2.5 Labile carbon fractions ..................................................................... 137 5.2.5.1 Hot water extractable-C........................................................... 137 5.2.5.2 Microbial biomass-C ............................................................... 138 5.2.5.3 Hydrogen peroxide oxidisable-C............................................. 138 5.2.5.4 Potassium dichromate oxidisable-C ........................................ 139 5.2.5.5 Potassium sulphate extractable-C ............................................ 139 5.2.6 Recover percentage of the added residue ......................................... 139 5.2.7 Statistical analysis ............................................................................ 139 5.3 Results ........................................................................................................... 140 5.3.1 Soils .................................................................................................. 140 5.3.2 Decomposition of added ryegrass pasture residues .......................... 140 5.3.3 Residue-C recovery .......................................................................... 146 5.3.4 Labile carbon fractions ..................................................................... 149 5.4 Discussion ..................................................................................................... 155 5.4.1 Decomposition of added ryegrass pasture residues .......................... 155 5.4.2 Residue-C recovery .......................................................................... 155 5.4.3 Labile carbon fractions ..................................................................... 156 5.5 Summary and conclusions ............................................................................. 159 ix 5.6 References ..................................................................................................... 160 Chapter-6 Short term test plus development of predictive decomposition simulation model.......................................................................................... 165 6.1 Introduction ................................................................................................... 165 6.2 Methodology ................................................................................................. 166 6.2.1 Simple regression model .................................................................. 166 6.2.2 Two and three compar tment decomposition models ........................ 167 6.2.2.1 Development of a temporally dynamic two compartment model using laboratory data .................................................... 167 6.2.2.2 Model efficiency...................................................................... 168 6.3 Results and discussion ................................................................................... 169 6.3.1 Simple regression model .................................................................. 169 6.3.2 Application of two compartment model to predict CO 2 fluxes in laboratory for full measurement period ........................................ 172 6.3.3 Application of temporally dynamic three compartment model to field data ....................................................................................... 179 6.3.4 Development of temporally and climatically dynamic three compartment model .......................................................................... 181 6.3.4.1 Development of temperature scalar ......................................... 181 6.3.4.2 Development of moisture scalar .............................................. 182 6.3.4.3 Application of temporally and climatically dynamic three compartment model to field data ............................................. 186 6.4 Summary and conclusions ............................................................................. 189 6.5 References ..................................................................................................... 191 Chapter-7 Conclusions and recommendations for future work ................................ 195 7.1 Conclusions ................................................................................................... 195 7.1.1 Selection of appr opriate method for measuring CO 2 fluxes............. 195 7.1.2 Reduction in CO 2 emissions with No-tillage ................................... 196 7.1.3 Influence of soil type and residue input rates on CO 2 emissions ..... 197 7.1.4 Modelling laboratory CO 2 fluxes to predict field CO 2 emissions ..... 198 7.2 Contribution to New Zealand agriculture ...................................................... 199 7.3 Future research .............................................................................................. 199 Appendix 1 .................................................................................................... 201 x List of Tables Table 2.1 Differences in soil organic carbon (SOC) between No - tillage (NT) and conventional plough tillage (CT) across a range of soils in different countries ........................................................................................................ 16 Table 2.2 Summary of the results of studies conducted on tillage induced CO 2 fluxes ............................................................................................................. 24 Table 3.1 Effect of absorption intervals on the CO 2 fluxes using closed static chambers with NaOH (alkali) traps of varying molarities ............................ 81 Table 3.2 Effect of absorption intervals on CO 2 fluxes using closed static chambers with NaOH (alkali) traps of varying molarities compared with spot measurements made by EGM -1 equipped with SRC-1 ........................ 82 Table 3.3 Carbon dioxide fluxes (mg CO 2 -C m -2 hr-1 ) under lower (negative) and ambient pressures in laboratory conditions during 4 and 12 hour measurement period ...................................................................................... 95 Table 3.4 Accumulated carbon dioxide emissions (mg CO 2 -C m -2 ) over 4, 12 and 16 hour measurement period under lower (negative) and ambient pressures in laboratory conditions ................................................................ 95 Table 4.1 Physico-chemical characteristics of th e five soil sites (0-10cm) .................. 105 Table 4.2 Cumulative average CO 2 - C emissions from simulated tillage (ST), No- tillage (NT) and Non - Disturbed (ND) treatmen ts from Glen Oroua, Tangimoana, Kiwitea and Feilding site soils till day -54 .............................. 114 Table 4.3 Pearson correlation coefficients of the total amount of CO 2 - C lost till day 54 during laboratory incubation from ST treatment and different soil factors of the four test soils ........................................................................... 115 Table 4.4 Multiple linear regression model between CO 2 -C emissions during laboratory incubation and gravimetric moisture content (GMC), total carbon content (TC) and residues in corporated (RES) for studied ST treatment on the four test soils ...................................................................... 115 Table 4.5 Coefficient of determination (R 2 ) comparing the CO 2 -C fluxes from the RT, NT and ND treatments with grav imetric moisture content (GMC %) and soil temperature during the autumn and summer seasons ...................... 119 Table 5.1 Chemical characteristics of the four soils (0 -10c m) used in the incubation study ............................................................................................ 135 Table 5.2 Percentage of added residue- C respired at different times of the incubation in the four different soils amended with different rates of pasture residues ............................................................................................. 145 Table 5.3 Percentage of added residue-C decomposed after 129 days in the four xi different soils amended with different rates of pasture residues................... 145 Table 5.4 Light and heavy fraction - C as influenced by pasture residue addition and recovery (%age) of added C after 129 days of the incubation study ............ 147 Table 5.5 Light and heavy fraction?s -C at beginning (day- 0) and after 129 days incubation study in control treatments of the four soils ................................ 148 Table 5.6 Labile C fractions (mg C g -1 soil) determined on the control treatments of the four soils at the beginning (day-0) of the incubation study ................ 149 Table 5.7 A Coefficients of determination comparing the CO 2 - C respired during 129 days incubation period with the labile C fractions determined on the control treatments of the four soils ............................................................... 150 Table 5.7 B P-values comparing the slopes for different labile- C fractions of Carnarvon SL, Ohakea stony SiL, Ohakea SiL with the slope of Horotiu SiL. ................................................................................................................ 150 Table 5.8 Hot water extractable -C at the beginning (day- 0) of the incubation study in the four soils as influenced by pasture residue additions.......................... 151 Table 5.9 Hot water extractable - C after 129 days of the incubation study in the four soils as influenced by pasture residue additions .................................... 151 Table 5.10 Microbial biomass -C at the beginning (day-0) of the incubation study in the four soils as influenced by pasture residue additions .............................. 152 Table 5.11 Microbial biomass - C after 129 days of the incubation study in the four soils as influenced by pasture residue additions ........................................... 152 Table 5.12 P- values comparing the slopes of the Carnarvon SL, Ohakea stony SiL, Ohakea SiL with the slope of Horotiu SiL for microbial biomass-C (MBC) and hot water extractable - C (HWC) determined at different residue rates .................................................................................................. 153 Table 5.13 Summary of the analysis of vari ance of the amounts of microbial biomass-C (MBC) and hot water extractable- C (HWC) determined on day-0 ................................................................................................ ............. 153 Table 5.14 Amount of carbon oxidised by 2% H 2 O 2 during 2 hour oxidation period at 35 oC from the four soils ............................................................................ 154 Table 6.1 Linear regression coefficients comparing the CO 2 -C evolved during full laboratory incubation period ( 54 to 99 days) with CO 2 - C evolved; until day 2, 4, 6, 8, 10 and 12 using soils from five sites ...................................... 169 Table 6.2 Two carbon pools (Pool -C and A), their respective decay constants (k c and k a) and coefficient of determination (R 2 ) of the daily measured and predicted (modelled) CO 2 -C fluxes for 12 days for develo ping the two compartment model ...................................................................................... 174 xii Table 6.3 Coefficient of determination (R 2 ) of the daily measured and predicted (modelled) CO 2 - C fluxes for the full measurement period of the five soils used in laboratory incubation study after application of the two compartment model ...................................................................................... 174 Table 6.4 Three carbon pools (Pool -C, A and B), their respective decay constants (k c, k a and k b) and coefficient of determination (R 2 ) of the daily measured and predicted (modelled) CO 2 - C fluxes for 12 days for developing the three compartment model ..................................................... 177 Table 6.5 Coefficient of determination (R 2) of the daily measured and predicted (modelled) CO 2- C fluxes for full measurement period of the five soils used in laboratory incubation study after application of the three compartment model ...................................................................................... 178 xiii List of Figures Figure.1.1 Thesis structure ............................................................................................. 5 Figure.2.1 Relative fractional change in soil C under No- tillage (NT) compared to conventional tillage (CT) as function of soil depth....................................... 20 Figure.2.2 Relative fractional change in soil C under No- tillage (NT) compared to conventional tillage (CT) in 0-30 cm soil depth ................................ ........... 20 Figure.3.1 Comparison of CO 2 fluxes measured by NaOH trap, EGM - 1 and closed static flux gradient method s .......................................................................... 84 Figure.4.1 Disturbed and undisturbed area by Cross Slot ? opener in the soil core ....... 107 Figure.4.2 Air temperature and precipitation at Sanson field site from April 2010 to February 2011 ............................................................................................... 112 Figure.4.3 Daily average CO 2 - C fluxes as influenced by tillage (ST, Simulated tillage; NT, No -tillage and ND, Non- Disturbed) from (a) Glen Oroua, (b) Tangimoana, (c) Kiwitea and (d) Feilding site soils ............................... 116 Figure.4.4 Cumulative average CO 2 - C emissions from simulated tillage (ST), No- tillage (NT) and Non-Disturbed (ND) treatments from Glen Oroua, Tangimoana, Kiwitea and Feilding site soils ................................ ................ 117 Figure.4.5 Daily average CO 2 - C fluxes as influenced by tillage (RT, Rotary tillage; NT, No -tillage and ND, Non- Disturbed) during (a) autumn and (b) summer season from Sanson field site ................................ .......................... 120 Figure.4.6 Daily average CO 2 -C fluxes from the rotary and No- tilled soils during the summer season from the Sanson field site affected by soil GMC .......... 121 Figure.4.7 Gravimetric moisture content (GMC %age) at 0 - 10 cm depth during 4 hour CO 2 measurements (a) autumn and (b) summer season ....................... 122 Figure.4.8 Soil temperature at 0 -10 cm depth for 4 hour CO 2 measurement period during (a) autumn season and (b) summer season ........................................ 122 Figure.4.9 Daily average CO 2 - C fluxes as influenced by tillage (ST, Simulated tillage; NT, No -tillage and ND, Non- Disturbed) during (a) autumn under field conditions and (b) summer season under laboratory conditions .......... 124 Figure.4.10 Cumulative average CO 2 - C emissions as influenced by tillage (ST, Simulated tillage; NT, No -tillage and ND, Non-Disturbed) durin g (a) autumn and (b) summer season ..................................................................... 124 Figure.5.1 The CO 2 -C emission rates from (a) Carnarvon SL and (b) Ohakea stony SiL as influenced by residue application during 12 9 days of laboratory incubation ...................................................................................................... 142 xiv Figure.5.2 The CO 2 -C emission rates from (a) Ohakea SiL and (b) Horotiu SiL as influenced by residue application during 12 9 days of laboratory incubation ...................................................................................................... 143 Figure.5.3 Cumulative CO 2 -C emissions from Carnarvon SL, Ohakea stony SiL, Ohakea SiL and Horotiu SiL ......................................................................... 144 Figure.5.4 Relationsh ips between cumulative CO 2 - C respired during 129 days incubation from the four soils at different residue rates with (a) microbial biomass-C and (b) hot water extractable-C measured at the beginning (day-0) of the incubation study ................................ .................... 153 Figure.6.1 The relationship between the total CO 2 - C evolved from simulated tillage and No -tillage soils until day-12 with CO 2 -C evolved during full incubation period ( 54 to 99 days) from the five soil sites during laboratory incubation study ........................................................................... 171 Figure.6.2 The relationship between the total CO 2 - C evolved from simulated tillage and No -tillage soils until day- 12 under laboratory conditions with total CO 2 -C evolved from rotary tillage (RT) and No-tillage (NT) soils for the full measurement period ( 99 days) for Sanson field site .............................. 171 Figure.6.3 Comparison of observed and predicted (modelled) CO 2 - C fluxes by two and three compartment models for 12 days (a, b) and full 92 days (c, d) measurement period for the Glen Oroua site for the simulated tillage (ST) treatment ............................................................................................... 175 Figure.6.4 Carbon dioxide contribution from pools - A & C of (a) two compartment model and pools-A, B and C of (b) three compartment models for the simulated tillage (ST) treatment for the G len Oroua soil site ....................... 176 Figure.6.5 Comparison of observed and predicted (modelled) CO 2 - C fluxes by the three compartment model for 99 days measurement period for the Sanson site from (a) rotary tillage (RT) and (b) No- tillage (NT) treatments ...................................................................................................... 180 Figure.6.6 Comparison of observed and predicted (modelled) cumulative CO 2 -C emissions by the three compartment model for 99 days measurement period for the Sanson site from (a) rotary tillage (RT) and (b) No - tillage (NT) treatments ............................................................................................. 180 Figure.6.7 Temperature scalar for the three compartment model .................................. 182 Figure.6.8 Moisture scalars for the three compartment model ....................................... 184 xv Figure.6.9 Comparison of observed and predicted (modelled) CO 2 - C fluxes by the three compartment model including temperature and moisture factors for the Sanson site from (a) rotary tillage (RT) and (b) No- tillage (NT) treatments for 99 days measurement period ................................................. 188 Figure.6.10 Comparison of observed and predicted (modelled) cumulative CO 2 -C emissions by the three compartment model including temperature and moisture factors for the Sanson site from (a) rotary tillage (RT) and (b) No -tillage (NT) treatments for 99 days measurement period ....................... 188 List of Plates Plate.3.1 Closed static chambers: lower pressure and ambient pressure set up .......... 93 1 Chapter-1 Introduction 1.1 Issue Soil organic carbon (SOC) has long been studi ed due to its beneficial effect on physical, chemical and biological properties of soils, and is frequently re ported as an indicator of soil quality and environmental sustainability (Ngwira et al. 2012). Rising environmental concerns about global warming, mainly due to increased CO 2 emissions, has increased awareness about the potential for SOC storage and greenhouse gas mitigation in agricultural soils (Ussiri and Lal 2009). Soil organic C is the largest active terrestrial C pool (>1550 Pg to 1m depth), with more than twice the C present in the atmospheric pool (Batjes 1996; Lal 2004), and is composed of both living and de composed forms of plant tissues and soil microbes (Christopher et al. 2009). Soil is a dynamic component of the global C cycle and may behave as a source or sink of CO 2 depending upon the management practices (Ogle et al. 2005; Franzluebbers 2005). Currently, the concentration of CO 2 in the atmosphere is 390.5 ppm and is increasing at the rate of 2 ppm per year ( http://cdiac.ornl.gov/pns/current_ghg. html ). Although fossil fuel consumption is the major cau se of the increase in CO 2 concentration, agricultural activities have also been a significant contributor (Lal 2001 ; 2002). Carbon dioxide is regarded as one of the most dynamic greenhouse gases related to agriculture (Sauerbeck 2001). Agricultural activities like deforestation, the conversion of natural to agricultural ecosystems, biomass burni ng, and excessive tillage ( Lal 2004, 2007 ) lead to emissions of greenhouse gases, in particular CO 2 , from soils. Historically, soils have lost about 40 to 90 Pg C (estimated value) to the atmosphere (Lal 2001; Smith 2008). Full cultivation has been regarded as a major cause of this C loss in cropping soils (Reicosky 2003), and tillage induced SOC loss in gene ral, has been well documented (Huggins et al. 2007; Hermle et al. 2008). The depleted SOC in agricult ural cropping soils can be restored by eliminating tillage, decreasing fallow peri ods and incorporating cover crops in the crop rotation cycle (Paustian et al. 2000; Jarecki and Lal 2003). No-tillage is a conservation man agement practice in which there is least soil disturbance (nearly 90 per cent of the soil surface re maining untouched), and whic h conserves both SOC and soil nutrient stocks in comparison to convent ional tillage. This practice has been adopted worldwide to combat soil erosion, as it leads to the development of a protective layer of crop residues at the soil surface (Puget and Lal 2005; Sa et al. 2001; Mazzoncini et al. 2011). The 2 adoption of No-tillage has incr eased over the past few decades, as it allows farmers to capture efficiencies in crop production - saving time, money a nd energy. Specialised openers are used for No-tillage as soil disturbance is limited to a spot where seed would be placed. Generally, No-tillage openers create ?V?, ?U? and inverted ?T? shaped slots (Baker and Saxton 2007). New Zealand scientists develope d a unique inverted-T-shaped opener for No- tillage. The opener was first commercially available as the ?Baker Boot? which underwent further development to become the current Cross Slot ? opener. Several fi eld and laboratory trials have shown that Cross Slot ? openers cause less soil disturbance and better seedling emergence in comparison to other No-tillage openers (Baker and Saxton 2007 ). V-shaped slots created by double and triple discs are the most commonly used No-tillage openers worldwide, which makes No-tillage with inverted ?T? openers in New Zealand different from other countries. Overseas research, mostly conducted in North America, has compared the CO 2 emissions from cropping soils under No-tillage and conventional tillage systems, and shown that conversion to No-tilla ge seeding could reduce CO 2 emissions, currently estimated at 20.1 to 24.2 Mg CO 2 ha -1 yr-1 , by up to 3 Mg CO 2 ha -1 yr-1 (Omonode et al. 2007; Ussiri and Lal 2009 ). Currently, about 6 per cent (1556 mill ion hectares) of cultivated land in the world is under No-tillage, mostly in North and South America (Christopher et al. 2009; Mishra et al. 2010). Therefore there is the potential that further conversion to No-tillage systems and the adoption of appropriate management practices could enhance the role of soil as a major sink for C, and offset CO 2 emissions from fossil fuel combustion. In New Zealand, approximately 1 mi llion hectares are sown in agricultural crops annually. Quantitative information on potential changes in the soil CO 2 emissions with No- tillage compared to conventional tillage in New Zealand systems is currently limited. Undoubtedly, soil and climatic conditions and th e amount of plant residue at cultivation are the key factors that further modify soil emissi ons. However, the effect of these factors on CO 2 fluxes from conventionally and No-tilled cropping/pasture systems under a temperate environment, such as New Zealand, are not we ll documented, nor are th ey well quantified. The Kyoto Protocol aims to reduce greenhouse gases by setting legally binding emission targets for member countries. The provision for assigning carbon credits for carbon conserved in forestry (article 3.3) and agricultur al soils (article 3.4) under the Kyoto Protocol has made carbon a marketable commodity (Lal 2003). Carbon conserved in trees and soils can be traded as any farm produce, leading to an additional income for the farmer which can be used in soil restoration (Lal 2008). 3 Being a signatory to the Kyoto Protocol New Zealand has committed itself to reduce its greenhouse gas emissions to 1990 levels by 2012 . To manage its Kyoto commitments, New Zealand introduced a financial market-based approach known as the emissions trading scheme (ETS). The scheme introduces a price on greenhouse gas emissions to provide an incentive to people to reduce greenhouse gas (G HG) emissions. The unit of trade is the New Zealand Unit (NZU ) which is often referred to as carbon credits; and it is equivalent to one tonne of CO 2-eq emissions. The New Zealand ETS c overs six Kyoto Protocol managed greenhouse gases: carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), hydro fluorocarbons (HFCs), per fluorocarbons (PFCs) , and sulphur hexafluoride (SF6). So far New Zealand is the only country that has si gnalled its intent to include agricultural greenhouse gas emissions in its ETS but th is has been delayed until 2015. However, mandatory monitoring and reporting for agricultural methane and nitrous oxide emissions has already started as of 1 January 2012. No commitment has been made for agricultural related carbon dioxide emissions to be included in carbon trading due to a lack of techniques to monitor the emissions of carbon dioxide related to agricultural activities, in particular tillage. No-tillage is seen as a promising stra tegy to conserve soil carbon leading to lower CO 2 emissions. There is also the possibility of inclusion of soil carbon losses or gains in the future revisions of the agricultural ETS sc heme. Such inclusion would necessitate the development of a credible technique/tool for the farming community and policy makers in order to accurately determine and verify the reduction in soil C loss with No-tillage. This project aims to quantify short term C lo ss from soils due to the decomposition of crop and/ or pasture residues after tillage, and attempts to provide a simple tool of either a soil test or decomposition simulation model to predict this C loss. 1.2 Research Objectives ? To determine CO 2 emissions with Cross Slot ? No-tillage cultivation compared with full cultivation. ? To determine the soil textural influences on decomposition of crop residues to establish a relationship between soil texture, residue input and CO 2 emissions. ? To identify the labile C pools contributing to these losses and establish a relationship between the oxidisable C pool and CO 2 emissions. ? To develop a simple tool of either a soil test or decomposition simulation model that can quantify emissions reductions with Cross Slot ? No-tillage to give the end-user the tools 4 necessary to implement advice on the use of Cross Slot ? No-tillage to reduce the GHG footprint of arable farming in New Zealand. 1.3 Thesis structure Significant research effort has been unde rtaken globally to understand the reductions in CO 2 emissions and increased soil C storage from No-tillage practice. However in New Zealand, research on the reduction of CO 2 emissions from agricultural soils using No-tillage practice, in comparison to conventional tillag e, is limited. The single study conducted by Aslam et al. (2000) is not sufficient to devise strateg ies to conserve soil C and its loss as CO 2 . This thesis (Figure 1.1) begins with an introductory Chapter-1 which covers the research background, conceptual framework, objectives and significance of this study in relation to New Zealand conditions. Chapter-2 presen ts an analysis of the available literature on various tillage practices and their role in conservation of soil C, factors affecting C loss and different methods for measuring soil CO 2 emissions. Various physical, chemical and biological techniques to quantify the labile soil C fractions and the effect of wind (Venturi effect) on soil CO 2 fluxes are reviewed. The review concludes with an overview of No- tillage practices and highlights the need for a soil test to predict conservation of CO 2 with No-tillage. Chapter-3 provides a comparison between three chamber methods (static chamber alkali trap method, static chamber flux gradient method and dynamic chamber method i.e. EGM-1) to measure CO 2 emissions, which was used as the basis for choosing an appropriate method for the current study; this ch apter also examines the venturi effect on CO 2 emissions from tilled and No-tilled so ils under laboratory conditions. Chapter-4 quantitatively determines the loss of CO 2 from tilled soils under laboratory and field conditions in comparison to No-tilled soils. Chapte r-5 quantifies the effect of soil texture and residue rate on decomposition of residues to CO 2 and attempts to provide a simple test for end users to use to quantify the reductions in emissions using No-tillage. Chapter-6 develops a model to predict CO 2 emissions in field conditions using laboratory measurements; and Chapter-7 provides a summary of results from the previous chapters along with the main conclusions derived from the research undertaken during this Ph.D., and the recommended direction for future research. 5 Figure.1.1: Thesis structure. Chapter 3 Chamber method comparison and venturi effect Three chamber methods were compared in this study under laboratory conditions to choose the appropriate method to be used in the field. This chapter also quantifies the wind velocity effect on CO 2 emissions from tilled and No-tilled soils. Chapter 4 RT versus NT Carbon dioxide measurements were made from rotary tilled and No - tilled soils under laboratory and field conditions to quantify the loss of CO 2 from tilled soils compared to No-tilled soils. This chapter compares the residue input and textural effect on residue decomposition to CO 2 under laboratory conditions; and concurrently compares the relationship of CO 2 emitted during the entire incubation period with labile C fractions to see which fraction correlates with CO 2 emitted. Chapter 5 Soil test for CO 2 Chapter 1 Introduction The need for and significance of this PhD study is discussed in relation to New Zealand in this chapter. Chapter 6 Modelling CO 2 fluxes In this chapter the CO 2 fluxes measured under laboratory conditions were modelled to predict CO 2 fluxes under field conditions. Chapter 2 Review of Literature This chapter summarises the current literature on CO 2 emissions as affected by tillage; factors affecting carbon loss; methods of CO 2 measurements; and labile carbon fractions. Chapter 7 Summary and conclusions Conclusions of all experimental work are provided in this chapter. 6 1.4 Significance of this study This research will test the hypothesis that No-tillage cultivation is a management strategy for increasing soil C in cropping and pasture soils, and attempts to provide a mitigation tool to farmers and policy makers. This tool will allow quantitative determination of the influence of No-tillage practices in reducing CO 2 losses and maintaining soil C, in comparison to current conventional cultivation techniques practised in New Zealand. This study will provide a process-based understandi ng of the soil and climatic factors controlling CO 2 losses. 7 1.5 References Aslam, T., Choudhary, M. A., & Saggar, S. (20 00). Influence of land-use management on CO 2 emissions from a silt loam soil in New Zealand. Agriculture Ecosystems & Environment, 77 (3), 257 - 262. Baker, C. J., & Saxton, K. E. (2007 ). No-tilla ge Seeding in Conservation Agriculture. Cromwell Press, Trowbridge, UK: FAO United Nations. Batjes, N. H. (1996). Total carbon and nitrogen in the soils of the world. European Journal of Soil Science, 47 (2), 15 1-163. Christopher, S. F., Lal, R., & Mishra, U. (2009). Regional study of no-till effects on carbon sequestration in the Midwestern United States. Soil Science Society of America Journal, 73 (1), 207-216. Franzluebbers, A. J. (2005). Soil organic ca rbon sequestration and agricultural greenhouse gas emissions in the southeastern USA. Soil & Tillage Research, 83 (1), 12 0-147. Hermle, S., Anken, T., Leifeld, J., & Weisskopf, P. (2008). The effect of the tillage system on soil organic carbon content under moist, cold-temperate conditions. Soil & Tillage Research, 98 (1 ), 94-105. Huggins, D. R., Allmaras, R. R., Clapp, C. E., Lamb, J. A., & Randall, G. W. (2007). Corn- soybean sequence and tillage effects on soil carbon dynamics and storage. Soil Science Society of America Journal, 71 (1), 145-1 54. Jarecki, M. K., & Lal, R. (2003). Crop management for soil carbon sequestration. Critical Reviews in Plant Sciences, 22 (6), 471-502. Lal, R. (2001). World cropland soils as a sourc e or sink for atmospheric carbon. In D. L. Sparks (Ed.), Advances in Agronomy, Vol 71 (Vol. 71, pp. 145-191). Lal, R. (2002). Soil carbon dynamics in cropland and rangeland. Environmental Pollution, 116(3), 353-362. Lal, R. (2003). Global potential of soil carbon sequestration to mitigate the greenhouse effect. Critical Reviews in Plant Sciences, 22 ( 2 ) , 151-184. Lal, R. (2004 ). Soil carbon sequestra tion to mitigate climate change. Geoderma, 123 (1-2), 1 - 22. Lal, R. (2007 ). Carbon Management in Agricultural soils. Mitigation and Adaptation Strategies for Global Change, 12 , 303-322. Lal, R. (2008). Ca rbon Sequestration. Philosophical Transactions of the Royal Society B, 363, 815-830. 8 Mazzoncini, M., Sapkota, T. B., B?rberi, P., Antichi, D., & Risaliti, R. (2011). Long-term effect of tillage, nitrogen fertilization and cover crops on soil organic carbon and total nitrogen content. Soil and Tillage Research, 114 ( 2), 165-174. Mishra, U., Ussiri, D. A. N., & Lal, R. (2010 ). Tillage effects on soil organic carbon storage and dynamics in Corn Belt of Ohio USA. Soil and Tillage Research, 107 (2), 88-96. Ngwira, A., Sleutel, S., & Neve, S. D. (2012). So il carbon dynamics as influenced by tillage and crop residue management in loamy sand and sandy loam soils under smallholder farmers? conditions in Malawi. Nutrient cycling in agroecosystems, 92 , 315 -328. Ogle, S. M., Breidt, F. J., & Paustian, K. (2005). Agricultural management impacts on soil organic carbon storage under moist and dry climatic conditions of temperate and tropical regions. Biogeochemistry, 72 (1), 87-121. Omonode, R. A., Vyn, T. J., Smith, D. R., Hegymegi, P., & Gal, A. (2007). Soil carbon dioxide and methane fluxes from long-term tillage systems in continuous corn and corn-soybean rotations. Soil & Tillage Research, 95 (1-2), 182-195. Paustian, K., Six, J., Elliott, E. T., & Hunt, H. W. (2000). Management options for reducing CO 2 emissions from agricultural soils. Biogeochemistry, 48 (1), 147-163. Puget, P., & Lal, R. (200 5). Soil organic carbon a nd nitrogen in a Mollisol in central Ohio as affected by tillage and land use. Soil & Tillage Research, 80 (1-2 ), 201-213. Reicosky, D. C. (2003). Tillage induced CO 2 emission and carbon sequestration: Effect of secondary tillage and compaction. In L. Garcia -Torres, J. Benites, A. Martinez-Vilela & A. Holgado-Cabrera (Eds.), Conservation Agriculture (pp. 291-300 ). Dordrencht, The Netherland: Kluwer Academic Publishers. Sa, J. C. D., Cerri, C. C., Dick, W. A., Lal, R., Venske, S. P., Piccolo, M. C., et al. (2001). Organic matter dynamics and carbon sequestrati on rates for a tillage chronosequence in a Brazilian Oxisol. Soil Science Society of America Journal, 65 ( 5 ), 1486- 149 9. Sauerbeck, D. R. (2001). CO 2 emissions and C sequestration by agriculture - perspectives and limitations. Nutrient Cycling in Agroecosystems, 60 (1-3), 253-266. Smith, P. (2008). Land use change a nd soil organic carbon dynamics. Nutrient Cycling in Agroecosystems, 81 (2), 169-178. Ussiri, D. A. N., & Lal, R. (2009). Long-ter m tillage effects on soil carbon storage and carbon dioxide emissions in continuous corn cropping system from an alfisol in Ohio. Soil & Tillage Research, 104 (1), 39-47. 9 Chapter-2 Review of literature 2.1 Soil carbon contribution to global warming The atmospheric concentration of the three major terrestrial greenhouse gases, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) have increased markedly from 280 to 390.5 ppm (CO2), 700 to 1750 ppb (CH4) and 270 to 322 ppb (N2O) (http://cdiac.ornl.gov/pns/current_ghg.html), since the industrial revolution as a result of human activities. The majority of climate change scientists around the world agree that increasing concentrations of greenhouse gases in the earth?s atmosphere are contributing to global warming and climate change (IPCC 2007). Concerns about global warming have increased interest in soil organic carbon (SOC) storage to counterbalance the rising atmospheric CO2 levels (Sanaullah et al. 2011; Ussiri and Lal 2009). Soil organic C plays a pivotal role in soil functional processes, through its influence on most biological, physical and chemical processes in the soil. The decrease in SOC due to changes in land use and land management practices can critically affect the sustainability of our productive soil resource (Lal 2002). Historically, ploughing of natural soils resulted in the considerable loss of 55 Pg (billion tons of C) from the global SOC pool thereby converting a large fraction of SOC to CO2 (Pacala and Socolow 2004). A loss of 55 Pg of SOC represents approximately 8.0% of the SOC (724 Pg) contained in the world?s soils up to 0-30 cm depth (Batjes 1996); most of the SOC is lost from surface soils as ploughing does not go beyond 30 cm of soil depth (Lal 1991). Under some circumstances adoption of conservation tillage, growing cover crops, and using organic manures can restore SOC thus making soil a net sink. No-tillage is regarded as an effective management practice in conserving SOC and reducing soil CO2 emissions, and has so far received the most attention in North and South America. In New Zealand, about 20% of annual crop and pasture establishment is undertaken using No-tillage seeding (Personal Communication C J Baker 2009). Given the importance of SOC to crop production, the impact of tillage on SOC storage has been well documented (Lal and Kimble 1997; West and Post 2002; Huggins et al. 2007; Ussiri and Lal 2009; Dalal et al. 2011). However, in New Zealand previous research on soil tillage measured the changes in soil physical properties as affected by No- tillage (Francis et al. 1987; Horne et al. 1992; Francis and Knight 1993; Hermawan and Cameron 1993; Ross et al. 2002), but little information is available on comparative effects of 10 No-tillage and conventional cultivation systems on CO2 emissions in New Zealand cropping systems. Based on the North America?s estimates of CO2 savings and estimated areas of No- tillage in New Zealand, No-tillage has a potential of conserving approximately 2.4-3.0 million tonnes of CO2 per year in New Zealand (see appendix 1).This amount of conserved CO2 could reduce the contribution of agriculture to total greenhouse gas emissions by 7.0% per year in New Zealand (detailed explanation in Chapter-7). The possibility of inclusion of soil C losses or gains in any future revisions of the agricultural emissions trading scheme (ETS) has increased interest in the use of No-tillage practice. The aim of this chapter is to review the role of traditional tillage practices in loss of soil C, various soil & climatic factors affecting soil C loss and labile C fractions contributing to this loss, and concludes by presenting the main research needs. Therefore this chapter first describes the range of current conventional tillage practices commonly used by farmers, and how these practices influence soil C dynamics. It then outlines various climatic & soil factors affecting soil C turnover and various methods of measuring soil CO2 fluxes. 2.2 Common tillage practices A tillage practice describes the nature and sequence of tillage operations used in preparing a seed bed for planting (Acquaah 2005). Tillage practices vary depending on soil characteristics, the crop to be grown, agro-ecological environment, and the socio-economic status of the farming community (Lal 1991; Koller 2003; Plaster 2009). Three basic goals of tillage are: 1) weed control 2) alteration of physical soil conditions, and 3) incorporation of the crop residues into the soil (Leij et al. 2002; Conant et al. 2007). Conventional tillage practices consist of primary cultivations to break the soil mass into a loose system of clods of mixed sizes followed by secondary cultivation for further pulverisation, repacking and smoothing of the soil surface, and is still used as the preferred tillage option (Koller 2003). Tillage is specifically designed to optimize edaphological conditions (soil water and temperature regimes, soil aeration, seed-soil contact, nutrient availability, porosity and pore size distribution and minimal incidence of pests) for seed germination, seedling establishment and crop growth (Lal 2004). In this practice natural soil structure is destroyed and a lot of energy is wasted (Blanco and Lal 2008). 11 2.2.1 Conventional tillage Conventional tillage (CT), also called ?intensive tillage?, comprises a series of tillage operations to prepare a seed bed that leaves less than 15% of crop residues on the soil surface after planting the next crop (Blanco and Lal 2008). Conventional mouldboard ploughing followed by secondary tillage operations is the most common and preferred tillage practice in the world (Lal 2004; Acquaah 2005). During primary tillage operations, topsoil is ploughed to a depth of 15 to 36 cm and inverted, burying the vegetation and debris on the soil surface. Depth and time of primary tillage depends upon soil type, soil moisture, nature and amount of crop residues on soil surface, climatic conditions and crop season. Commonly used primary tillage implements are the mouldboard plough, disc plough, chisel plough, and powered rotary tiller. Secondary tillage operations are performed at shallow depths from 5 to 15 cm with an aim to improve seedbed level, moisture conservation, and increased soil pulverisation. Ploughing (primary tillage), disking and harrowing (secondary tillage) are carried out in sequence in order to produce a fine seedbed for seeding (McKyes 1985; Acquaah 2005; Plaster 2009). 2.2.2 Conservation tillage Conservation tillage and conservation agriculture are collective umbrella terms given to No-tillage, minimum tillage and/or any other non-inversion soil tillage practice that has a conservation goal of some nature (Baker and Saxton 2007). Conservation tillage practices may retain at least 30% or more of the soil surface with crop residue after planting, improve soil structure, conserve soil moisture, reduce soil erosion, increase soil organic matter and improve environmental quality and agricultural sustainability (Lal 1991; Choudhary and Baker 1994; Acquaah 2005; Govaerts et al. 2009). Different conservation tillage systems as described by Acquaah (2005); Baker and Saxton (2007); Blanco and Lal (2008) and Plaster (2009) are as follows: 1) Mulch tillage (mulch-till) : The soil is disturbed by tillage prior to seeding. A chisel plough, which loosens the soil but does not invert, is used as the primary tillage followed by light disking and seeding, leaving about 30-50 per cent of the crop residues. Weeds are controlled by a combination of cultivation and herbicides. 2) Strip tillage (strip-till or zone-till) : The soil is not disturbed prior to seeding. About one- fourth of the soil is tilled at the seeding time in a narrow strip ahead of the drill openers; so that seed is sown in the strip of tilled soil but the soil between the sown rows remain undisturbed, leaving behind approximately 50 per cent of crop residues. This tillage system 12 is suitable for poorly drained soils. Weeds are controlled by a combination of cultivation and herbicides. 3) Ridge tillage (ridge-till) : The soil is left undisturbed from harvest to planting except for fertilizer application. In ridge tillage system, 15 cm high ridges are formed by tillage during the second cultivation or after harvest in preparation for following year?s crop. Seed is planted on 15 cm ridge tops with crop residues swept into shallow furrows. About two-thirds of crop residues remain after planting. Ridges may remain in place for several seasons or they might be reformed annually. Weeds are controlled by a combination of cultivation and herbicides. 4) Minimum tillage (reduced tillage) : Any conservation tillage practice that minimizes the total number of primary and secondary tillage operations for seeding and leaves at least 30% residue cover after planting to control soil erosion and sustain crop production. 2.2.3 No-tillage No-tillage (NT) (no-till, direct drilling, direct seeding, zero tillage, slot-till and slot planting) is a practice in which soil disturbance is limited only to a spot where seed would be placed. The soil is left undisturbed from harvest to planting. About 90 per cent of the soil surface is untouched i.e. minimal soil disturbance, which maximizes the benefits of conservation tillage. This practice commonly retains 70-95% of surface residues intact. Weeds are controlled primarily by herbicides. No-tillage preserves soil organic matter (SOM) near the soil surface, reduces greenhouse gas production and stores the most soil C in comparison to other tillage systems (Lal 2004). However, No-tillage has some disadvantages as it results in a build-up of grass weeds, increases pest and disease problems, decreases crop yields due to poor germination and uneven crop growth, and requires specialised machinery which requires up-skilling. In addition, No-tillage can influence nitrous oxide (N2O) emissions from soils (Ball et al. 2008). There are contradictory results regarding the impact of No-tillage on N2O emissions. Higher (Ball et al. 1999; Beheydt et al. 2008) and lower (Chatskikh and Olesen 2007; Gregorich et al. 2008) N2O emissions were observed from No-tillage soils than for tilled soils. Reviews conducted by Rochette (2008) and Powlson et al. (2012) suggest that soil aeration, which in turn is a combination of soil type and rainfall, and timing of nitrogen fertilizer application, are the key factors determining the N2O emissions from soils. Conventional mouldboard plough followed by secondary cultivation has been the traditional method for New Zealand farmers to establish crops and pasture (Hamilton-Manns 13 et al. 2002). Repeated cultivation leads to degradation of soil structure and loss of soil C as CO2 due to decomposition of plant residues, exposing previously protected SOC in aggregates to micro-organisms (Jimenez and Lal 2006). Soil C has become important in recent times due to greenhouse gas emissions and climate change. This review suggests that the No-tillage is receiving a lot of global attention as an alternative tillage practice to conserve SOC (Puget and Lal 2005). Large numbers of farmers are turning to No-tillage farming to stop the loss of valuable topsoil by erosion; to curb the run-off of sediment, fertilizer, and pesticides into rivers and lakes; to save time, money, and energy; and to conserve SOC (Lal et al. 2007). However, successful implementation and adoption of No-tillage strongly depends upon the farmers? knowledge of the technology, soil and climatic conditions of a given area. 2.3 Effect of tillage practices on soil carbon dynamics 2.3.1 Carbon storage Tillage is an integral part of traditional agriculture and has strong impact on soil organic carbon (SOC) storage (Bayer et al. 2000; Reicosky 2003). Gregorich et al. (1998) found that most soils lost about 20-30% of SOC following 20 years of cultivation, and that maximum loss occurs within the first five years. Lal (2002) estimated that many soils in the Midwestern United States have lost 30-50% of SOC in about 50 years, which they contained prior to cultivation and which has been attributed to conventional cultivation practices. Recent studies recommended conversion from conventional to No-tillage as an efficient strategy to offset the stimulating effect of C emissions on global warming (Puget and Lal 2005; Luo et al. 2010). West and Post (2002) used a global database of 67 long-term agricultural experiments, and analysed data of 93 paired conventional and No-tillage plots. They estimated that, on average, changing from conventional mouldboard plough tillage to No- tillage can restore SOC at the mean rate of 0.57?0.14 Mg C ha-1yr-1 leading to new equilibrium SOC pool in 40-60 years. Similarly, with 96 comparisons of contrasting tillage systems in the south-eastern United States, Franzluebbers (2005) estimated that moving from conventional to No-tillage can restore SOC at the mean rate of 0.42?0.46 Mg C ha-1yr-1. Both of these studies focussed on C change in surface soils (< 30cm) so this may simply be the artefact of sampling methodology (Blanco-Canqui and Lal 2008; Lal 2009). Baker et al. (2007) have shown examples where No-tillage had little effect on C storage particularly when deeper (>30cm) soil depths were considered i.e. when the whole soil profile (0-60/100 14 cm) is considered No-tillage plots contained similar or lower amounts of C in comparison to conventional mouldboard plough plots. Angers et al. (1997a) observed significantly higher SOC in conventionally tilled plots in comparison to No-tillage plots near the bottom of plough layer in several sites in eastern Canada. The data from 24 studies are presented in Table 2.1, and shows the change in SOC at different soil depths due to No-tillage; the majority of these studies were conducted in North America. The duration of the experiments varied from 2 to 44 years and soil texture varied from loamy sand to clay. Studies included in this table differed in the tillage methods used such as chisel plough, disc plough, and mouldboard plough, and in crop, fertilizer and residue management practices. The statistical significance of the results by depth (as reported in the original studies) are stated in Table 2.1 (last two columns). These results suggest that SOC under No-tillage was significantly greater in surface soil layers (0-5; 0-7.5; 0-10 cm) and significantly lower in deeper soil layers (20-30; 20-40; 30-50 cm) in comparison to conventional tillage. The 24 studies reviewed are presented as a scatter plot (Figure 2.1). Each data point represents the relative soil C change. The relative soil C change at each depth was calculated as: (NT-CT)/CT????.?????????.. (Eq. 2.1) where, NT= SOC at the end of the experiment for NT soil (Mg C ha-1) and CT= SOC at the end of the experiment for CT soil (Mg C ha-1) Due to differences in the depth of soil sampling between studies (0-5; 0-10; 0-15; 0- 20; 0-30; 0-40; 0-45; 0-60; 10-15; 10-20; 10-30; 15-30; 20-30; 30-40; 5-10; 5-15; 5-20 cm), each data point in the graph (Figure 2.2) represents the relative soil C change at the mean depth of the soil layer from which soil was collected (e.g. a soil sample collected from a 20- 30 cm core has a mean depth of 25 cm). At each depth, the student?s t value was used to test the hypothesis that the relative soil C change was zero (using SAS version 9.1). In general, relative soil C change data point values were >0 for 0-15 cm suggesting lower soil C stock under CT than NT at that depth (Figure 2.1). The relative soil C change was significantly greater than zero at 0-5, 0-10 and 0-15 cm depth intervals (p<0.05) i.e. the cultivation zone. The surface accumulation of SOC with NT practice irrespective of texture and duration of experiment has been well documented (West and Post 2002). With increase in depth intervals many data points? values were lower than 0, suggesting that average soil C stock could be greater under CT than NT at lower depths. However, at lower depth intervals (10- 30, 15-30, 20-30, 30-40 cm) the relative soil C change values were not significantly different 15 from zero (p> 0.05). This suggested that the tillage effect on SOC is stratified with soil depth. The presence of carbon in the form of crop residue on the soil surface is beneficial because surface residue accumulation has shown to reduce wind and water erosion, moderate soil temperature effects, conserve soil moisture, and provide an energy source for soil microorganisms (Lal and Kimble 1997; Govaerts et al. 2009). Franzluebbers (2002 a; b) suggested that the degree of change in SOC with depth can be used as an indicator of ?soil quality?, because surface accumulation of organic C is essential to control erosion, water infiltration, and the conservation of nutrients. Furthermore it provides energy, substrates, and the biological diversity necessary to sustain numerous soil functions. From the available data set, 13 comparison studies which measured SOC to at least 30 cm are presented in a scatter plot (Figure 2.2). Soil organic C stocks in 0-10, 10-20, 20-30 cm depths were summed and then relative soil C changes (Eq. 2.1) were calculated. In general, when the soil depth (0-30cm) was considered the soil C change with No-tillage showed a weak but significant correlation with duration of the experiment (Figure 2.2). Only a small proportion of variation (R2=0.32) was explained by this factor; however the relationship seems to be biased as the majority of studies have a short duration. Moreover, the important factor which affects the relative impacts of tillage practices on soil C change is crop residue inputs. No meta-analysis could be conducted on this factor due to insufficient data. Results of NT and CT comparisons can be variable; Christopher et al. (2009) observed a change from -20.3 to 22.8 Mg ha-1 in the carbon stock in 0-60cm depth from 12 paired NT and CT experiments varying in duration from 5-23 years in USA. Similarly, Angers and Eriksen-Hamel (2008) analysed data of 237 paired SOC depth measurements and suggested that No-tillage significantly increased the SOC in surface soil layers whereas conventional tillage has higher SOC near the bottom of the plough layer (23 cm). However higher SOC stocks at bottom of plough layer were not able to offset the gain under No-tillage in surface layers, resulting in higher SOC stocks under No-tillage. In conclusion literature suggests significantly higher SOC stocks at the soil surface with No-tillage cultivation. Greater SOC content at deeper soil layers with conventional tillage practices did not completely nullify the gain at the soil surface with No-tillage; however, the view that No-tillage cultivation increases SOC over conventional tillage practices is questionable when sampling depth is considered. 16 T a b le 2 .1 : D if fe re nc es in s oi l o rg an ic c ar bo n (S O C ) be tw ee n N o- til la ge ( N T ) an d co nv en tio na l p lo ug h til la ge ( C T ) ac ro ss a r an ge o f so ils in di ff er en t c ou nt ri es , m os t o f th e st ud ie s re po rt ed in th is ta bl e us ed m ou ld bo ar d pl ou gh e xc ep t f ew a nd a re a rr an ge d by p ub lic at io n ye ar . S o il t ex tu re L o ca ti o n D u ra ti o n (y ea rs ) P lo u g h d ep th (c m ) D ep th o f sa m p li n g (cm ) L a y er s sa m p le d ? S OC (M g C h a -1 y r- 1 ) R ef er en ce D ep th a t w h ic h s ig n if ic a n t d if fe re n ce s (p <0 .0 5 ) i n S O C w er e fo u n d N T >C T N T C T N T C T N T C T N T 2pa inside the chamber was observed by Suleau et al. (2009) from a forest floor. Takle et al. (2004) observed that pressure changes due to wind speed and direction penetrated in soil to depths of 45 to 60 cm and found that CO2 fluxes at the surface to be approximately three times what would be expected from calculations based on diffusional flux rates. Reicosky et al. (2008) found higher CO2 concentrations in No-tillage soils in comparison to ploughed soils and suggested that tillage induced changes in the soil bulk density and air porosity enabled wind speed to affect the gas exchange and resulted in a rapid decline in the CO2 concentration. The review suggests that pressure differences created by wind speed inside the chambers or on the soil surface results in an increase in CO2 fluxes from soils. 24 T a b le 2 .2 : Su m m ar y of th e re su lts o f st ud ie s co nd uc te d on ti lla ge in du ce d C O 2 fl ux es . So il T ex tur e Ca rbo n c on ten t (g C kg -1 s oil ) Lo ca tio n Du rat ion CO 2-C lo ss du rin g CT (kg ha -1 d -1 ) CO 2-C lo ss du rin g NT (kg ha -1 d -1 ) ?C O 2 -C lo ss (kg ha -1 d -1 ) Re fer en ce C la y lo am 31 .7 * (0 -7 .6 c m ) U SA 19 d ay s 13 1. 05 26 .2 6 10 4. 79 R ei co sk y an d L in ds tr om (1 99 3) C la y 22 .6 # (0 -3 0 cm ) U SA 1 da y 11 5. 00 18 .2 0 96 .8 0 R ei co sk y et al. (1 99 7) Si lt lo am 15 .0 U SA 60 d ay s 4. 80 2. 50 2. 30 D ao ( 19 98 ) Sa nd y lo am 19 .9 C an ad a 97 d ay s 40 .1 0 27 .6 0 12 .5 0 R oc he tte a nd A ng er s (1 99 9) L oa m 18 .0 ( 0- 7. 5 cm ) C an ad a 2 da ys 13 .0 3 10 .3 2 2. 71 E lle rt a nd Ja nz en ( 19 99 ) Sa nd y lo am 15 .5 ( 0- 30 c m ) A rg en tin a 40 d ay s 58 .2 5 43 .7 5 14 .5 0 A lv ar ez et al. ( 20 01 ) L oa m 29 .0 (0 -1 5 cm ) U SA 20 d ay s 6. 97 4. 09 2. 88 A l- K ai si a nd Y in ( 20 05 ) L oa m y Sa nd 13 .1 * (0 -7 .6 c m ) U SA 51 d ay s 28 5. 05 41 .2 4 24 3. 81 B au er et al . (2 00 6) 24 25 So il T ex tur e Ca rbo n c on ten t (g C kg -1 s oil ) Lo ca tio n Du rat ion CO 2-C lo ss du rin g CT (kg ha -1 d -1 ) CO 2-C lo ss du rin g NT (kg ha -1 d -1 ) ?C O 2 -C lo ss (kg ha -1 d -1 ) Re fer en ce Si lt lo am 28 26 .0 * g m -2 ( 0- 20 c m ) Sp ai n 16 d ay s 10 5. 30 52 .0 0 53 .3 0 A lv ar o- Fu en te s et al. ( 20 07 ) L oa m y Sa nd 20 .3 ( 0- 20 c m ) D en m ar k 91 da ys 40 .1 1 30 .0 0 10 .1 1 C ha ts ki kh an d O le se n (2 00 7) Si lt lo am 10 .0 (0 -3 0 cm ) T ur ke y 46 d ay s 11 .7 8 1. 96 9. 82 A kb ol at et al. ( 20 09 ) L oa m 7. 5* (0 -2 8 cm ) Sp ai n 8 da ys 9. 50 6. 30 3. 20 M or el l e t a l. (2 01 0) Si lt lo am 35 .3 (0 -1 0 cm ) N ew Z ea la nd 1 ye ar 60 .0 0 62 .5 0 -2 .5 0 A sl am et al . (2 00 0) Si lty c la y lo am 6 (0 -2 0 cm ) U SA 2 ye ar s 19 .8 0 20 .7 0 -0 .9 0 Fr an zl ue bb er s et al. ( 19 95 ) Sa nd y cl ay lo am 32 .3 * (0 -2 0 cm ) U SA 17 m on th s 27 .6 4 32 .1 3 -4 .4 9 H en dr ix et al. ( 19 88 ) ?C O 2- C lo ss = C O 2- C lo ss f ro m c on ve nt io na l t ill ag e m in us C O 2- C lo ss f ro m N o- til la ge *i nd ic at es a ve ra ge c ar bo n co nt en t f ro m tw o co nt ra st in g til la ge s ys te m s # in di ca te s ca rb on c on te nt f ro m th re e cr op pi ng s ys te m s 25 26 2.3.3 Carbon fractions Six et al. (1998, 2000b, 2002a) concluded that a reduced rate of macro-aggregate turnover under No-tillage in comparison to conventional tillage led to the formation of stable micro-aggregates that increased C storage under No-tillage in the longer term. Many techniques based on chemical, physical and biological separation have been used to measure the size and turnover of SOM pools (von Lutzow et al. 2007). Physical fractionation has been widely used to study the turnover of SOM pools because of its direct relationship with SOM structure and function compared to chemical fractionation which is not useful in following the dynamics of SOM in natural and cultivated systems (Golchin et al. 1994a; Duxbury et al. 1989; Christensen 2001; Dou and Hons 2006). Physical fractionation of soils on the basis of density and/ or size of particles, allows the separation of uncomplexed/unprotected SOM (Christensen 2001; Six et al. 2002a). Two most commonly isolated forms of physically uncomplexed/unprotected SOM are the light fraction (LF) and particulate organic matter (POM) (Gregorich et al. 2006). The LF is isolated by floatation in a high density liquid (density range 1.4-2.2 g cm-3) and POM by size (> 53 ?m), or by combination of both size and density fractionation procedures (Christensen 1992; Christensen 2001). Both LF and POM are composed of plant residues, but also contain microbial debris such as fungal hyphae and spores, and are considered to be sensitive indicators of the effects of tillage and cropping practices (Janzen et al. 1992; Cambardella and Elliott 1992; Golchin et al. 1994b; Boone 1994; Hassink 1995; Magid et al. 1997; Dominguez et al. 2009). Other measurable C fractions are the heavy fraction (HF) and mineral-associated fraction; these are organomineral complexed SOM, which is more stabilized than POM and LF therefore less sensitive to soil management (Alvaro-Fuentes et al. 2008). Adoption of No-tillage can lead to the accumulation of POM through increases in macro-aggregation (Beare et al. 1994a, b; Six et al. 1998, 1999, 2000b; Chan 2008). Cambardella and Elliott (1992) measured the percentage of total organic C present as POM- C and found 39, 18, 19 and 25% in sod, bare fallow, stubble mulch and No-tillage after 20 years for a loam soil, respectively. They suggested that No-tillage reduced POM loss caused by tillage and aeration. After 13 years of continuous No-tillage and conventional mouldboard plough with grain sorghum [Sorghum bicolour (L.) Moench] and winter rye (Secale cereal L.), Beare et al. (1994b) found that No-tillage had 20% more POM-C than mouldboard plough. Tiessen and Stewart (1983) observed a 43% loss of POM after 4 years of continuous 27 tillage and concluded that POM is important in macro-aggregate formation, and it is this labile fraction that was affected by tillage. Similar results have been observed in a number of studies (Bayer et al. 2001; Mrabet et al. 2001; Bayer et al. 2002; Bayer et al. 2006; Alvaro-Fuentes et al. 2008; Huang et al. 2010). Zhang et al. (2007) found that cultivation resulted in a decrease in LF-C and HF-C but the decrease in LF-C was considerably greater than the change in HF-C showing the LF- C as a fraction sensitive to management induced changes in organic carbon. Several researchers found similar results while studying the impact of No-tillage and conventional tillage on C dynamics (Yang and Kay 2001; Freixo et al. 2002; Roscoe and Buurman 2003; Soon et al. 2007; Tan et al. 2007; Murage et al. 2007; Zotarelli et al. 2007). The review above suggests that physical fractionation is commonly used to study the turnover of the soil organic matter in cultivated and No-tillage soils. Soil C conserved by No-tillage in the light fraction (LF) and particulate organic matter (POM) may result only in temporary C storage because it could easily be lost through microbial decomposition with change in management practice. 2.4 Factors affecting soil carbon loss Carbon dioxide emissions from soil to the atmosphere are the primary mechanism of C loss from soils and a pathway in the terrestrial C budget (Parkin and Kaspar 2003). The emission of CO2 from soils is controlled by many factors such as the amount of crop residues added, C: N ratio of the added residues, crop rotations, total C content of the soil and added residues, labile soil-C fractions, tillage history of soil, fertilizer management, manure application, soil temperature, moisture and texture. In the research to follow, on the effect of tillage techniques, many of these factors will remain constant therefore this review considered soil temperature, moisture and texture, to understand their effects on the crop residue decomposition. In addition to these, soil tillage practices affect the production and release of CO2 by influencing the soil physical conditions (Reicosky et al. 1997; Ball et al. 1999). 2.4.1 Soil temperature Temperature is the key factor controlling soil CO2 fluxes (Maljanen et al. 2002; Parkin and Kaspar 2003; Dilustro et al. 2005; von Lutzow and Kogel-Knabner 2009). Soil temperature depends upon atmospheric/air temperature and is influenced by presence/ absence of vegetation, water content and depth within soil (Aslam 1998). Soil temperature is 28 the primary rate determinant of microbial processes and a central input to models simulating the effects of mineralisation of soil C pools on global warming (Parton et al. 1987). According to their temperature requirements micro-organisms are divided into three groups - cryophiles, mesophiles and thermophiles - with their respective optimum temperatures being <20, 20 to 40, and >40?C (Luo and Zhou 2006). Soil microbial activity is considered to be negligible at temperatures <5?C (Wood 1989). There is an exponential increase in soil respiration with respect to temperature increases observed for biological systems (O?Connell 1990). Seasonal variations in the CO2 emissions under long term field measurements are attributed to changes in soil temperature both under forest (Longdoz et al. 2000; Liang et al. 2004) and agricultural soils irrespective of the tillage systems (Aslam et al. 2000; Sanchez et al. 2002; Bauer et al. 2006; Omonode et al. 2007; Elder and Lal 2008; Alvaro-Fuentes et al. 2008; Ussiri and Lal 2009; Almagro et al. 2009) i.e. higher CO2 emissions were measured in the summer season in comparison to the autumn/winter season due to higher temperatures in the summer season than autumn and winter season. Diurnal variations in CO2 efflux are correlated to soil temperature (Jensen et al. 1996) with maximum CO2 fluxes measured during mid-afternoon and minimum during early morning (Akinremi et al. 1999; Shi et al. 2006). Laboratory incubations provide the best estimates of temperature dependence of soil organic matter decomposition (Kirschbaum 1995). Field soil organic matter decomposition estimates are biased due to confounding factors of soil moisture and seasonal substrate availability, and the contribution of root respiration, which is not separated from that of soil microorganisms and fauna in the field studies (Reichstein et al. 2005). There is a range of problems associated with field measurements, but with laboratory measurements there are no such problems (Kirschbaum 2006). However, there is unease about accepting the results from controlled temperature under laboratory conditions for predicting responses under field conditions. Due to the complexity of the processes involved in soil organic matter decomposition both laboratory and field studies should be done simultaneously to test whether laboratory results can be extrapolated to the field. The mathematical relationship between soil respiration and temperature is often described as the Q10 relationship/ Q10 factor or temperature coefficient, where the Q10 factor is the factor by which soil respiration increases for 10oC rise in temperature. Under controlled laboratory conditions, Sato and Seto (1999) found that the rate of CO2 evolution from forest and arable soils increased exponentially with increase in the incubation 29 temperature from 4 to 40oC. The temperature coefficients (Q10) for forest and arable soils were 2.0 and 1.9. A similar rise in CO2 production was observed by Kechavarzi et al. (2010) with increase in temperature from 22 to 30oC under laboratory conditions for peat soils, the mean Q10 value was 3.2. Yuste et al. (2007) and Rey et al. (2008) in separate incubation studies on forest soils from U.S and Europe observed higher C mineralization rates with increase in temperature from 4-35oC, average Q10 values were 2.0 and 2.98 for U.S and European soils. Kirschbaum (1995) reviewed many laboratory decomposition studies in which temperature varied and soil CO2 evolution was measured in the absence of plants to assess the effect of temperature on organic C decomposition. He observed increased laboratory decomposition rates with increasing temperatures; Q10 values varied from 2 to 8, with lower Q10 values at higher temperatures. These laboratory decomposition experiments were used to further develop the temperature-response function to be used in soil organic matter simulation models (Kirschbaum 2000). 2.4.2 Soil moisture Soil moisture is another important factor influencing soil respiration and hence CO2 emissions (Rastogi et al. 2002). Water content in soil is essential for growth and activity of microbes, affecting the availability of substrates such as organic C, ammonium and nitrate that are essential for microbial functions (Weitz et al. 2001). Soil water content influences the concentration of O2 and CO2 in the soil profile (Aslam 1998) and soil temperature (Davidson et al. 1998; Akinremi et al.1999). Respiration rates under high soil moisture contents are regulated by O2 concentration (Linn and Doran 1984; Skopp et al. 1990). Bouma and Bryla (2000) suggested that high soil water content obstructs CO2 production in soils by limiting both O2 and CO2 diffusion. Low soil water content, below a certain limit can hinder the soil microbial activity and root respiration because the aquatic habitat is reduced (Davidson et al. 1998; Xu and Qi 2001; Tang and Baldocchi 2005). Luo and Zhou (2006) suggested that optimum water content is near field capacity, where macro-pores are mostly air-filled, thus facilitating O2 diffusion and micro-pores are mostly water-filled thus providing a habitat for bacteria and fungi and facilitating the diffusion of soluble substrates. In addition, low soil moisture content strongly limits the response of soil respiration to soil temperature. At low soil moisture content, high temperature/warmer soil conditions decrease soil CO2 fluxes (Reichstein et al. 2002; Xu et al. 2004; Tang and Baldocchi 2005; Yuste et al. 30 2007) due to a decrease in soil metabolic activity. A rapid increase in soil CO2 fluxes within minutes after precipitation and rewetting of dry soils has been observed in a variety of ecosystems (Sponseller 2007; Jenerette et al. 2008; Almagro et al. 2009; Morell et al. 2010). A flush of microbial growth in the rewetted soil influences the soil CO2 fluxes during the wetting and drying cycle. The rapid release of CO2 immediately after precipitation is due to the infiltration of rainfall water into the soil pores, which replaces highly concentrated CO2 air, resulting in degassing (Alvaro-Fuentes et al. 2008). Degassing is not considered to be soil respiration as it releases the stored CO2 in soil from past microbial and root respiration. Microbial activity increases from several hours to a few days following precipitation, with an up to 40% increase in microbial cell counts observed within 2 days after precipitation (Saetre and Stark 2005). Adu and Oades (1978) argued that the rapid pulse of CO2 is derived from microbial oxidation of labile soil organic C substrates derived from the physical disruption of soil aggregates due to the wetting and drying cycle, whereas Fierer and Schimel (2003) suggested that the pulse of CO2 is generated by the rapid mineralization of highly enriched intracellular compounds as a response of the microbial biomass to the rapid increase in the soil water potentials. 2.4.3 Soil texture Soil texture or size distribution of primary particles is the most influential quality of soil (Brady and Weil 2007). On the basis of the percentages of sand, silt and clay, 12 soil texture types are characterised globally (Eswaran 2003). Soil texture, particularly soil clay content, influences soil CO2 emissions by affecting pore-size distribution and pore continuity, which in turn controls soil water availability, gas movement by diffusion and mass flow and microbial activity (Scott et al. 1996; McInerney and Bolger 2000; Thomsen et al. 2003; Galantini et al. 2004). The interaction of clay mineral type and clay content with soil C is very important in the process of formation of stable soil aggregates and soil structure (Six et al. 2000c). Monitoring soil CO2 fluxes from sandy and clayey forest soils for three years, Dilustro et al. (2005) found that soil moisture and respiration were significantly correlated in sandy soils but less so in clayey soils. This was because soil respiration was suppressed in sandy soils during warm and dry periods in comparison to fine textured clayey soils, which have more water holding capacity resulting in slow release of moisture during warm periods. Bouma and Bryla (2000) measured soil CO2 fluxes from three different soil texture mixtures varying from 1 to 28% clay under laboratory conditions. They found that the rate of CO2 31 release in sandy soil after rewetting returned to pre-wetting rates more quickly than in fine textured soils. They suggested that this may be due to lower water holding capacity of sandy soils resulting in the diffusion of CO2 more freely through air-filled pores. Soil C storage is positively correlated with the clay content of soil (Burke et al. 1989; Bird et al. 2003; Galantini et al. 2004; Tan et al. 2004; Plante et al. 2006; Homann et al. 2007; Kong et al. 2009). Within the same climatic area, with similar inputs, higher microbial biomass and more organic matter are expected in soils with high clay content than with low clay content (Hassink 1994; Muller and Hoper 2004). Because of the large specific surface area and negative charge, clay particles interact with organic matter to form stable clay- organic complexes that make organic matter less susceptible to decomposition (Plante et al. 2006). Numerous studies conducted in the past to determine the influence of soil texture (clay content) on organic residue decomposition (Sorensen 1983; Ladd et al. 1981, 1985, 1992; Sharkov and Bukreeva 2004) have shown that in comparison to sandy soils, fine textured soils (with higher clay content) have lower rates of residue decomposition and retain higher proportions of residues by complexing with decomposition products, thereby reducing the losses of residue C from soil. Saggar et al. (1999) in a laboratory incubation study found that soil clay content and clay surface area played a significant role in controlling the decomposition of added C14 labelled glucose through stabilisation and protection of microbial biomass. Saggar et al. (1996), using two silt loam soils (clay content 16 and 24%) and two clay soils (clay content 56 and 60%) varying in mineralogy and surface area, found that, during the first 9 weeks of the decomposition in micro-lysimeters under field conditions, a greater portion of 14C-labelled ryegrass was retained in clay soils than in silt loam soils. After 5 years, the amount of remaining 14C was better correlated with the soil surface area than clay content alone. However, some other studies reported a weak or non- existent relationship between soil carbon storage/ decomposition and clay content (Scott et al. 1996; Hassink 1997; Percival et al. 2000; Wang et al. 2010). The review suggests that the loss of soil carbon as CO2 is interactively affected by multiple factors and it is often difficult to separate these interactions. Carbon dioxide production in soil is directly controlled by soil temperature and moisture by their influence on microbial activity. The rate of CO2 production and release depends upon temperature over a wide range of moisture contents but becomes responsive to moisture content as soil dries out below a certain limit. Soil texture particularly clay content influences CO2 fluxes by modifying pore-size distribution and pore continuity, which in turn affects soil water 32 availability and gas diffusion. Soil C content is positively correlated with clay content, as the clay content increases the capacity of the soil to protect organic matter against decomposition. 2.5 Methods for measuring carbon dioxide fluxes Soil surface CO2 flux measurements have been widely used to construct ecosystem C budgets (Norman et al. 1992) although accurate measurements of soil CO2 flux are difficult to obtain (Lund et al. 1999). There are several methods available to measure CO2 flux, including chamber and micrometeorological techniques. These methods vary in their accuracy, spatial variability and applicability. To date, there is no one recognised standard for accurate measurements (Rayment and Jarvis 1997). Therefore, methods are often chosen based on a trade-off between accuracy and feasibility. 2.5.1 Chamber methods Chamber methods are the most commonly used method to measure CO2 flux. Chamber methods directly measure CO2 flux at the soil surface. Chamber size is a critical factor, which must be considered in designing measurement protocols i.e. sampling intervals. In addition either a chamber with adequate geometry or an increased number of chambers may be required to integrate the spatial variability across the soil surface (Rochette and Hutchinson 2005). To cope with the spatial variability, either a history of measurements is required or a reconnaissance study is required to design the chamber size and number. 2.5.1.1 Alkali trap approach One chamber technique is the alkali trap approach. This is the oldest method of measuring soil CO2 flux, which was introduced by Lundegardh (1927), and which is often known as static chambers, absorption chambers, or alkali trap chambers. This approach uses a chemical absorbent (alkali trap) to capture CO2 released from the soil into the sealed chamber headspace. There are three common alkali traps: NaOH, KOH or soda lime (NaOH and Ca(OH)2) (Buyanovsky et al.1986; Jensen et al. 1996; Janssens and Ceulemans 1998; Grogan 1998). The total mass of CO2 trapped at the end of the absorption period is determined by titrating the alkali solution with dilute HCl. The CO2 flux (total amount of CO2 trapped over the absorption period) is then calculated using the following calculation: F= (Ctrap-Cblank)/t A???????????..? (Eq. 2.2) 33 Where Ctrap is the amount of CO2 trapped; A is the surface area covered by the chamber; t is the absorption period; and the use of a blank (Cblank) accounts for any bias due to the contamination of the alkali solution (Kabwe et al. 2002). Minderman and Vulto (1973) tested various alkali concentrations (0.25 - 2.0M) under field conditions and suggested that when sufficient alkali is used (approximately 30 ml) increasing the concentration from 0.25 to 2.0 M did not affect the CO2 fluxes measured for a 24 hour interval. In order to achieve efficient CO2 absorption, Kirita (1971) recommended a 13% alkali/chamber area ratio, depending upon chamber design and deployment conditions. Gupta and Singh (1977) suggested that absorption area has no significant effect on flux rates when at least 35% of the alkali remains unused after CO2 absorption. However, the appropriate concentration of alkali to use will depend upon the expected CO2 flux. The use of static chambers with alkali traps eliminates any convective or venturi effects that might happen during the field CO2 flux measurements as molecular diffusion is the only major process of CO2 exchange in this technique. 2.5.1.2 Flux gradient approach The closed static chamber, flux gradient approach is a method in which there is no movement or replacement of air in the sealed chamber headspace. This means that the CO2 gas concentration will increase steadily (Liang et al. 2004; Denmead 2008). The rate of increase is monitored by taking gas samples using polypropylene syringes which are fitted with 3-way stopcocks (Hutchinson and Mosier 1981). The samples are taken after the chambers are sealed, at different time intervals with the first sample taken at time zero (t0) (Pumpanen et al. 2003, 2004; Liang et al. 2004). The gas samples are then transferred to evacuated vials and analyzed using gas chromatography (GC) (Bekku et al. 1997; Saggar et al. 2004). Carbon dioxide flux (mg m-2 hr-1) is estimated from the measurements made at different time intervals. The sample of the ambient air taken just after closing the chamber (t0) is used as a reference for calculating CO2 gas fluxes. During gas sampling by syringe, the chamber must be tightly sealed so that the gas extraction samples are not contaminated with ambient air. Secondly, size of the gas sample withdrawn should be small relative to the chamber headspace to get a reliable flux value with this technique. A large sample size may create negative pressure in the chamber leading to biased flux values. The advantage of using static chambers is that they are easy to use and inexpensive; however they are highly labour intensive due to the large number of chambers required to 34 account for temporal and spatial variability under field conditions. Long-term placement of the chambers may also lead to changes in the soil microclimate inside the chamber. 2.5.1.3 Closed dynamic chamber approach In closed dynamic systems, air is circulated between the chamber and infrared gas analyser (IRGA). Once the chamber covers the soil surface, the CO2 concentration in the chamber rises due to the release of CO2 beneath the soil surface. The rate of CO2 release is proportional to the soil CO2 flux. Soil CO2 flux is calculated by measuring the difference between CO2 values at the start and end of the measurement period (Rochette et al. 1992, 1997; Rayment 2000). Closed dynamic chambers are advantageous as the CO2 fluxes can be measured quickly, with minimal effect on enclosed soil surface temperature and moisture content. This system of flux measurements is very labour intensive when used manually, but can be automated and run for long periods of time to collect data, although this makes it costly to use. This method involves the insertion of a soil collar several centimetres into the soil to accommodate the chamber and to prevent CO2 leakage out of the chamber (Hutchinson and Livingston 2001). A soil collar must be inserted in advance, a day before the CO2 flux measurement to avoid the effect of the disturbance associated with the insertion of the collar. However, this is a concern for systems with long closure times and/or known pressure artefacts (Heinemeyer et al. 2011). 2.5.1.4 Open dynamic chamber approach In dynamic open chamber systems, fresh air of a known concentration of CO2 flows into the chamber while an equal volume of air is withdrawn. The air leaving the chamber has a higher concentration of CO2 relative to the air entering the chamber, due to the CO2 released from the soil surface. This concentration is measured by infrared gas analyzer (Kanemasu et al. 1974) and soil CO2 flux is calculated using the flow rate and difference in CO2 concentrations (Fang and Moncrieff 1996, 1998). This approach is also known as the steady state through flow chamber technique. It provides continuous CO2 measurement with high accuracy due to its steady state. However, pressure differences inside and outside the chamber may lead to underestimation (at over pressurization or higher pressure) or overestimation (at under pressurization or lower pressure) of CO2 fluxes (Pumpanen et al. 2001; Liang et al. 2004). 35 2.5.2 Micrometeorological techniques Micrometeorological techniques are based on the concept that gas transport from the soil surface is accomplished by eddies that move air parcels from the soil to the measurement height. They also assume that vertical flux, measured at the reference level, is identical to the flux from the soil (Mosier 1990; Janssens et al. 2000; Baldocchi 2003). Micrometeorological measurement methods include eddy covariance, flux gradient and mass balance calculations (Denmead 2008). Micrometeorological techniques have advantages over chamber systems as they achieve spatial and temporal integration of CO2 flux, and do not modify the microenvironment of the soil surface (Dugas 1993; Janssens et al. 2000). They can also measure soil CO2 flux over long periods and integrate large areas (Baldochhi 1997, 2003). However, successful applications of micrometeorological techniques are dependent on homogeneous upwind fetch and appropriate weather conditions (Baldocchi and Meyers 1991). In addition, the presence of vegetation between the soil and the measurement height may alter the measured fluxes (Norman et al. 1997) and a key limitation to its use is the high cost of instrumentation. It is unlikely that micrometeorological technique will replace widespread use of chamber methods as the most common means of measuring soil CO2 efflux (Norman et al. 1997). From the review it is concluded that different techniques have been used to measure soil CO2 fluxes, but to date there is no universal consensus on one standard method. In the past, different methods have been compared throughout the world either against known CO2 fluxes or directly on the soil surface (Rochette et al. 1992, 1997; Nay et al. 1994; Norman et al. 1997; Bekku et al. 1997; Jensen et al. 1996; Le Dantec et al. 1999; Janssens et al. 2000; Yim et al. 2002; Pumpanen et al. 2003, 2004; Liang et al. 2004). Several comparison studies suggest that each technique has its own strengths and weaknesses, depending on its application. Chambers are the most commonly used method of measuring CO2 fluxes due to their simple operating principle, flexibility, portability and low cost. Literature suggests that we may expect chamber and micrometeorological techniques to be combined in the future, in order to achieve an accurate measurement of CO2 fluxes with spatial and temporal integration. 2.6 Nature of soil organic carbon and separation techniques Soil organic matter (SOM) is composed of plant, animal and microbial residues at various stages of decomposition and all intimately associated with soil inorganic components, and is an indicator of soil fertility and productivity. The content of organic 36 matter in soil changes with any change in land use and soil management practices, particularly labile C fractions. A range of techniques are used to measure and characterize soil organic C in relation to its dynamics under various management practices. Various chemical and physical techniques are discussed in the context of soil organic C turnover rates with change in soil management practices. In a Ph.D. study at Massey University, Bhupinder-Pal Singh (2000) undertook an intensive review of the chemical and physical characterisation of SOM and discussed acid hydrolysis and oxidative techniques using potassium permanganate for labile-C extractions. He also discussed the density separation of soils. This review adds to the information provided in his thesis. In addition to potassium permanganate, the use of hydrogen peroxide, sodium hypochlorite and disodium peroxodisulphate are discussed in this review for oxidising the labile-C fractions. In addition to density separation, aggregate and particle size separations are discussed. Furthermore this review also provides the biological characterisation of SOM and various biological approaches used to estimate microbial biomass. 2.6.1 Chemical characterisation Historically, chemical fractionation of SOM is based upon the differences in solubility properties of humic substances in alkaline and acid solutions, which yields three major fractions: humic acids (HA), fulvic acids (FA) and humin (Kononova 1966). Fulvic acids are soluble in both alkali and acid, humic acids are soluble in alkali but precipitated by acid, and humins are insoluble in both acid and alkali (Collins et al.1997). Schulten and Schnitzer (1997) observed that humic and fulvic acids have similar chemical structures, however, in comparison to humic acids, fulvic acids have lower molecular weight and C content but higher oxygen content. Humins are condensed humic acids and consist of polysaccharides, phenolic or methoxy substituted aromatic structures and paraffinic structures (Hatcher et al. 1985; Hayes and Clapp 2001). They form strong complexes with clays and hydrous oxides and are not easily separated by alkali and acids (Schulten and Schnitzer 1997). Humic acids and humins have slow turnover rate, and are usually older (1130-1410 years) than the fulvic acids (50-550 years) (Cheng et al. 2007); however, Campbell et al. (1967) found a portion of humic acid and humin with short turnover rate (25- 465 years) indicating incomplete separation with conventional SOM fractionation. The total SOC can be differentiated into pools or fractions of varying stability (Zimmermann et al. 2007). Wander (2004) divided SOC into labile/active, slow/intermediate 37 and recalcitrant (passive/stable/inert) pools on the basis of biologically, physically and chemically regulated dynamics. However, different scientists have used different terms for these pools (Krull et al. 2003). A range of chemical fractionation techniques have been used to differentiate total SOC (Jagadamma et al. 2010) by acid hydrolysis (Leavitt et al. 1996; Collins et al. 2000; Rovira and Vallejo 2002; Cheng et al. 2007; Silveira et al. 2008) and chemical oxidation (Mikutta et al. 2005; Siregar et al. 2005; Helfrich et al. 2007; Jagadamma and Lal 2010). The aim of both fractionation techniques is to separate labile/active fractions from relatively stable/recalcitrant humic (fulvic acid, humic acid and humin) fractions (von Lutzow et al. 2007). However, there is no single appropriate chemical fractionation technique to isolate the labile and stable SOC fractions. Chemical oxidation causes changes in SOM composition which mimics biodegradation i.e. natural microbial oxidation processes (Helfrich et al. 2007; Eusterhues et al. 2003) and is more efficient in isolating labile/young SOC fraction than acid hydrolysis (Bruun et al. 2008). This review will focus on oxidisable SOC fractions as discussed in the coming section. 2.6.2 Chemical fractionation techniques Several oxidants like potassium permanganate (Loginow et al. 1987; Blair et al. 1995), hydrogen peroxide (H2O2) (Theng et al.1999; Plante et al. 2004; Favilli et al. 2008), disodium peroxodisulphate (Na2S2O8) (Eusterhues et al. 2003; Bruun et al. 2008) and sodium hypochlorite (NaOCl) (Siregar et al. 2005; Kleber et al. 2005; Zimmermann et al. 2007) are used in oxidising easily and strongly oxidisable C fractions. Potassium permanganate has been used to measure easily oxidisable/labile C fractions (Blair et al. 1995; Whitbread et al. 1998; Blair and Crocker 2000). Loginow et al. (1987) introduced the chemical separation of SOC based on its susceptibility to oxidation with various concentrations of KMnO4 (33-333mM). The method is based on the concept that the oxidative action of KMnO4 is similar to enzymatic breakdown of SOM which involves uptake of oxygen and liberation of CO2 i.e. an oxidative process (Blair et al. 1995; Bell et al. 1998). Lefroy et al. (1993) used three different concentrations (33, 167 and 333mM) of KMnO4 to measure soil C fractions to relate which fraction is more sensitive to cropping and observed a decline in the C fractions oxidised by all the three different concentrations (33, 167 and 333mM) of KMnO4 with long term cultivation, however, the major decline was observed in the fraction oxidised by 33mM KMnO4. 38 Blair et al. (1995) standardised and simplified the original KMnO4 oxidation technique introduced by Loginow et al. (1987) and suggested the use of one concentration of KMnO4 (333mM) to distinguish labile soil C (oxidised by 333mM KMnO4) from non-labile soil C (not oxidised by 333mM KMnO4). Blair et al. (1995) compared cropped and un- cropped soils and observed a major decline in the 333mM KMnO4-oxidisable C fraction. The decline was proportionally greater than the decline in total-C. The KMnO4 oxidises about 5- 35.2% of the total C (Blair et al. 1995; Conteh et al. 1997; Bell et al. 1999; Haynes 2005; Vieira et al. 2007; Chen et al. 2009; Saha et al. 2011). Murage et al. (2000) and Xu et al. (2011) observed a relationship between KMnO4 oxidisable C with water soluble, particulate and microbial biomass C in Kenyan and Chinese soils. However, Tirol-Padre and Ladha (2004) and Skjemstad et al. (2006) reported only a weak correlation between KMnO4 oxidisable C with water soluble, particulate and microbial biomass C. They suggested that KMnO4 rapidly oxidises less readily available organic compounds (like lignin) than water soluble carbohydrates and is not able to distinguish between labile and non-labile C and therefore should be referred to as permanganate-oxidisable-C (POC) when used as a parameter in characterizing soil. Hydrogen peroxide was proposed for soil texture analysis by Robinson (1922) and is a widely used oxidant to remove soil C to date. Eusterhues et al. (2005) used hydrogen peroxide and found that the oxidation resistant C fraction was 500-3900 years older than C in the bulk soil suggesting that hydrogen peroxide preferably oxidises younger C fractions. About 42 to 97% of SOC can be oxidised by hydrogen peroxide (von Lutzow et al. 2007). Sodium hypochlorite introduced by Anderson (1963) was used to remove soil organic matter for mineralogical analysis of clays. Sodium hypochlorite removes about 26-96% of SOC in soils (Kleber et al. 2005; Mikutta et al. 2005; Siregar et al. 2005). An increase in radiocarbon ages from 75-6350 years after treatment with sodium hypochlorite compared to bulk soils was observed by Kleber et al. (2005). Disodium peroxodisulphate was proposed by Meier and Menegatti (1997) to remove SOM and have a higher efficiency than hydrogen peroxide and sodium hypochlorite in oxidising soil C. Between 16-99% of SOC was oxidised by disodium peroxodisulphate (Eusterhues et al. 2003). The C fraction resistant to hydrogen peroxide, sodium hypochlorite and disodium peroxodisulphate treatment is aliphatic in nature (Leifeld and Kogel-Knabner 2001; Cuypers et al. 2002; Jagadamma et al. 2010). A wide range of per cent SOC that can be oxidised by hydrogen peroxide, sodium hypochlorite and disodium peroxodisulphate in soils was due to differences in the soil?s clay mineralogy (Kleber et al. 2005). 39 From the review it is concluded that chemical fractionation is very useful in understanding structure and composition of SOM. Chemical fractionation techniques help in measuring the relative distribution of labile and stable organic C fractions to monitor the effect of treatments. However, chemical fractionations have several limitations as chemical extracts used may modify SOM structure. Moreover, chemical extracts may isolate that fraction which may be physically protected from microbes and not available for decomposition, which cannot be termed as a labile fraction. Due to the complex nature of SOM, chemical fractionation may have limited success. 2.6.3 Physical characterisation Commonly chemical extractions are used to differentiate SOM fractions based on their respective solubilities in various extractants (acids and bases) and considers interaction between the SOM and soil mineral particles, chemical bonding within SOM but not the three dimensional arrangement of organo-mineral complexes (Six et al. 2000d; Olk and Gregorich 2006). Therefore, they are not very useful in identifying SOM fractions that reduce with intensive management (Collins et al. 1997) because turnover rates of different SOM fractions are partly controlled by microbial decomposition and are affected by both the chemical and physical nature of SOM (Bhupinderpal-Singh 2000). The physical and/or physicochemical binding between SOM and soil minerals is the mechanism supposed to be responsible for protection/stability and turnover rate of SOM fractions (Six et al. 2002b; Chan 2008). To study the C turnover rates, physical fractionation methods which are less destructive than chemical fractionation are employed (Christensen 1992) and results obtained from physical fractionation techniques are more strongly related to the structure and function of SOM in-situ (Golchin et al. 1994a). Physical fractionation methods are based on the premise that the association of primary soil particles with SOC and their spatial arrangement plays an important role in the dynamics of SOM (Gregorich et al. 2006; von Lutzow et al. 2007). Physical fractionation employs a number of different methods, each designed for a specific purpose, application of which, involves combinations of mechanical, chemical and ultrasonic dispersion for size fractionation (wet and dry sieving, slaking) and/or using heavy liquids (1.4-2.4 g cm-3) for density fractionation. Detailed reviews on methodology have been published by Elliott and Cambardella (1991); Christensen (1992) and Collins et al. (1997). The objective of every fractionation technique is to avoid chemical changes in SOM during the fractionation step and to provide a clear differentiation between separated SOM fractions. The recovery of SOM and soil material depends upon the complexity of the 40 physical fractionation. More complex the fractionation procedure, more are the chances of errors and losses of SOM (Abdul-Kader 2006). Different methods of physical fractionation are discussed in detail in the coming section. 2.6.4 Physical fractionation techniques 2.6.4.1 Aggregate fractions Aggregates are also referred to as secondary organo-mineral complexes and reflect the degree of aggregation of primary particles (Christensen 2001). The formation and dynamics of soil aggregates has been reviewed in detail by Golchin et al. (1997); Wander (2004); Six et al. (2004); Yadav and Malanson (2007). The aggregate hierarchy concept introduced by Tisdall and Oades (1982) described how primary mineral particles (<20 ?m) are bound together with microbial products, root exudates and polyvalent cations into micro- aggregates (20-250 ?m). These micro-aggregates are bound into macro-aggregates (>250 ?m) by transient (microbial and plant derived polysaccharides) and temporary (roots and fungal hyphae) binding agents. The hierarchy concept by Tisdall and Oades (1982) was modified by Oades (1984), who suggested that ?roots and hyphae which hold together the macro-aggregate, forms the nucleus for micro-aggregate formation in the centre of the macro-aggregate?. As roots and hyphae are temporary binding agents they crumble into fragments, get coated with mucilage produced during the decomposition process and became encrusted with clay particles and microbial products resulting in the formation of micro- aggregates within a macro-aggregate. Further studies by Golchin et al. (1994a); Beare et al. (1994b); Jastrow et al. (1996) supported the concept of the formation of micro-aggregates within macro-aggregates. In a field incubation study, Angers et al. (1997b) traced C and N in macro- and micro-aggregates during decomposition of C13 N15-labelled wheat straw and found reorganization of C13 from macro-aggregates to micro-aggregates with time. Carbon distribution in micro-aggregates happens only after distribution at the macro-aggregate level, indicating that micro-aggregate OM is older and in a much more stabilized state than macro- aggregate OM. Aggregate fractionation is based on the separation of free-OM and occluded (intra- aggregate) OM that is trapped within secondary organo-mineral complexes (Six et al.1998, 1999). Aggregate fractions are isolated by wet or dry sieving and slaking (Elliott and Cambardella 1991). Dry sieving checks the stability of aggregates against mechanical disruption when dry soil is rotary sieved and wet sieving includes the effect of the wetting process also (Christensen 2001). 41 Slaking is the breakup of large aggregates into smaller ones due to build-up of internal pressure during rapid wetting of soil (Six et al. 1999). Six et al. (2000b) proposed a fractionation technique of wet sieving and slaking to completely break up macro-aggregates while minimizing the breakdown of the released micro-aggregates (53-250 ?m). Six et al. (2000a) studied the effect of conventional and No-tillage on aggregation and aggregate associated-C and found that increased C under No-tillage was due to a reduced turnover rate of macro-aggregates in comparison to conventional tillage. Denef et al. (2004) suggested that more than 90% of the difference in SOC between conventional and No-tillage was because of a difference in micro-aggregate associated C. Various studies have shown that turnover times were about 100-300 years for OM in micro-aggregates (< 250 ?m) and 15-50 years for OM stored in macro-aggregates (> 250 ?m) (Puget et al. 2000; Six et al. 2002b; Yamashita et al. 2006). 2.6.4.2 Particle size fractions Particle size fractionation is based on the concept that SOM associated with particles of different size and mineralogical composition, differs in structure and function, and therefore plays different roles in SOM turnover. Particle size fractionation can be applied to whole soil or to heavy fractions following density fractionation (Christensen 1992). Particle size fractionation divides SOM into size classes by sieving (dry/wet depending upon the purpose of fractionation) and sedimentation following dispersion (Six et al. 1998; Wander 2004; Sleutel et al. 2006). Size fractionation separates soil into sand, silt and clay sized fractions related to texture of soil. Sand particles show weak bonding to SOM whereas clay particles provide large surface area and numerous reactive sites where SOM can be sorbed by strong ligand exchange and polyvalent cation bridges (Sposito et al. 1999). Based on the sorption stabilization mechanism, SOM in the sand fraction is more labile (active pool) than silt and clay fractions (intermediate and passive pool) (Evans et al. 2001; Chenu and Plante 2006). Various degrees of dispersion are used to break down macro- and micro-aggregates to separate uncomplexed OM and different size organomineral complexes (Christensen 2001; Gregorich et al. 2006). Sonication and shaking are commonly used dispersion techniques (Elliott and Cambardella 1991). Sonication produces vibration energy to the soil suspension causing cavitation, which disrupts bonding agents. Presently, there are no standard protocols for sonication, and it has potential to redistribute organic matter among size/density fractions (Collins et al. 1997). Shaking is a more gentle alternative dispersion 42 method to sonication (Elliott and Cambardella 1991). However, Christensen (1992) observed that high-speed shakers may cause abrasion of particles and simple shaking in water may not provide complete dispersion even after prolonged treatment periods. Chemical dispersants and prolonged shaking times can improve soil dispersion but chemically assisted dispersion can introduce unintended changes in SOM structure and should be avoided unless the action of the dispersant is specific and is well documented (Elliott and Cambardella 1991; Christensen 1992). Sieving, sedimentation and centrifugation are collectively used to determine particle size fractions (von Lutzow et al. 2007). Generally, about 50-75% of the total SOM is associated with the clay sized fraction (< 2 ?m), about 20- 40% with the silt sized fraction (2-20 ?m) and < 10% with the sand sized fraction (> 20 ?m) (Christensen 2001). 2.6.4.3 Density fractions Density fractionation techniques can separate SOM into two discrete fractions representing different stages of decomposition (Gregorich et al. 2006; Crow et al. 2007). Density fractionation is applied to the whole soil to separate the SOM fraction which is not firmly attached to soil minerals i.e. light fraction (LF) from organo-mineral complexes or the heavy fraction (HF) with floatation in inorganic salt solutions with a specific density varying from 1.4 to 2.2 g cm-3 and limited sample dispersion (Strickland and Sollins 1987; Christensen 1992; Crow et al. 2007). The light fraction of SOM has density <1.4 to 2.0 g cm- 3 (lower than that of soil minerals), is mineral free, and composed of partially decomposed plant and animal residues with a wider C: N ratio (Janzen et al. 1992; Boone 1994; Magid and Kjaergaard 2001). The light fraction is regarded as highly labile with a rapid turnover rate (Boone 1994; Gregorich et al. 2009) due to the labile nature of its constituents i.e. easily decomposable carbohydrates (Six et al. 2002b). The heavy fraction of SOM is composed of highly processed decomposed products with a narrow C: N ratio adsorbed on to the surface of silt & clay particles (Six et al. 2002b). The heavy fraction has a slower turnover rate and higher specific density > 2.0 g cm-3 due to strong close association with soil minerals (Meijboom et al. 1995; Olk and Gregorich 2006; Crow et al. 2007). Prior to density fractionation, various degrees of sample dispersion are used to break soil aggregates to separate uncomplexed OM and organo-mineral complexes (Christensen 1992). However, compared to particle size based fractionation, density based methods rely on less vigorous dispersions or on shaking (Wander 2004). Historically, heavy organic liquids have been used for density fractionation i.e. tetrabromoethane C2H2Br4 (2.96 g cm -3), 43 bromoform CHBr3 (2.88 g cm -3), tetrachloromethane CCl4 (1.59 g cm -3) but aqueous solutions of inorganic salts i.e. sodium Iodide NaI and sodium polytugstate Na6 (H2W12O40) have gained popularity as previously organic liquids were halogenated hydrocarbons that were highly toxic to humans (Christensen 1992; Crow et al. 2007). The light fraction has been suggested as the indicator of changes in labile organic C as affected by tillage, cropping practices and addition of crop residues (Janzen et al. 1992; Boone 1994; Magid et al. 1997; Six et al. 2002b; Liang et al. 2003; Soon et al. 2007; Zhang et al. 2007). Golchin et al. (1994a; b) modified the basic two-fraction method and separated LF located between soil aggregates (inter aggregate/ free LF) from protected intra-aggregate (occluded LF) by sonication and floatation and suggested that slow turnover of occluded LF compared to free LF is due to difference in stability. Murage et al. (2007); and Gregorich et al. (2009), while studying the effect of tillage practices on SOC fractions, observed higher turnover of C in free LF than occluded LF. In order to relate SOC fractions to stable soil aggregates to better understand the SOC dynamics, Six et al. (1998; 1999; 2000a) developed a complex technique using size and density fractionation to isolate organic C within soil aggregates having different roles in nutrient cycling and stabilization mechanisms. The density of plant residues changes during decomposition due to breakdown of vascular structure and loss of entrapped air. Therefore the density separation used to separate LF-C is also used to distinguish the nature and extent of organic matter decomposition (Gregorich et al. 2006) as a part of labile organic matter in soil originates from live and dead root & plant residues (Bhupinderpal-Singh et al. 2005). However, the ability of density separation techniques to recover decomposing residues is affected by the size of the residues and their interaction with mineral surfaces (Magid et al. 1996; Bhupinderpal-Singh et al. 2005). In order to overcome this problem, Magid et al. (2010) and Bhupinderpal-Singh et al. (2009) suggested using a combined size-density procedure to recover plant residues from soil rather than using a density procedure alone. The review suggests that physical separation techniques provide a differentiation between labile/active and passive soil carbon fractions. Particulate organic matter and/or the light fraction are representative of the labile pool whereas the mineral or heavy fraction is a mixture of intermediate and passive pools. Therefore, using density or combined size-density procedures would be helpful to identify the fate of the light fraction like plant or root residues during their decomposition. 44 2.6.5 Biological characterisation Soil microbial biomass is the living or/ active component of soil organic matter and is defined as the mass of soil micro-organisms < 5000 ?m3 in volume, which excludes soil animals and roots (Brookes et al. 2008; de Araujo 2010). Microbial biomass plays a crucial role in C, N, P and S transformations; acts as both a source and sink for nutrients; and contributes to soil structure (Lagomarsino et al. 2009). It is a biologically meaningful, easily measurable and management sensitive fraction (de Araujo and de Melo 2010). The turnover time of microbial biomass varies from 0.5 to 5 years in comparison to >20 years for total SOM (Dalal 1998; Nyamadzawo et al. 2009). Based on dynamics of soil microbial biomass content long-term trends in SOM can be predicted (Truu et al. 2008). Changes in microbial biomass content due to change in land use practices can be used as early predictors of changes in SOM (Powlson et al. 1987; Yeates and Saggar 1998; Saggar et al. 2001; Melero et al. 2006; Brookes et al. 2008). Soil microbial biomass is affected by input of organic residues as high amounts of organic inputs result in higher microbial biomass (Peacock et al. 2001; de Araujo and de Melo 2010). Soils in No-tillage cropping systems have higher microbial biomass in comparison to soil under conventional tillage perhaps due to higher crop residue input and increased SOC content, or, higher soil moisture contents (Aslam et al. 1999; Alvarez and Alvarez 2000; Doyle et al. 2004; Nyamadzawo et al. 2009). During a field trial comparing conventional and organic farming practices, Melero et al. (2006) observed significantly higher microbial biomass under organic than conventional management practices due to higher input of residues. Similar results were observed by Tu et al. (2006); Araujo et al. (2008) and Okur et al. (2009) while comparing conventional and organic management practices. In general, microbial biomass C ranges between 0.8 and 7.0% of SOC (Wardle 1992). In agricultural top soils, microbial biomass C ranges between 0.3 and 4.0% depending on the soil texture and tillage practices (von Lutzow et al. 2007). There are different techniques to determine soil microbial biomass C which are listed in the upcoming section. 2.6.6 Biological approaches Commonly used techniques to estimate soil microbial biomass are: * Direct microscopic counting (Jenkinson et al. 1976c) * Chloroform fumigation-Incubation (FI) (Jenkinson and Powlson 1976b) * Chloroform fumigation-Extraction (FE) (Vance et al. 1987) * Substrate induced respiration (Anderson and Domsch 1978; West and Sparling 1986) 45 * Adenosine triphosphate (ATP) analyses (Jenkinson and Oades 1979; Jenkinson et al. 1979; Tate and Jenkinson 1982) * Phospholipids fatty acids (PLFA) (Frostegard and Baath 1996) * Microwave irradiation (MW) (Islam and Weil 1998) Detailed descriptions of the methods and their advantages and limitations to estimate soil microbial biomass are given by Jenkinson (1988); Horwath and Paul (1994); Martens (1995); Dalal (1998); Brookes (2001); de Araujo (2010); Gonzalez-Quinones et al. (2011). Basic soil properties such as soil pH, soil moisture and organic matter level need to be considered while selecting a method to estimate soil microbial biomass. Among all the methods available, chloroform fumigation is the most commonly used method (de Araujo 2010; Gonzalez-Quinones et al. 2011) to estimate soil microbial biomass. In the chloroform fumigation method the fumigated soil is either incubated (Jenkinson and Powlson 1976b) or extracted (Vance et al. 1987). The chloroform fumigation extraction technique has been successfully used to determine N (Brookes et al. 1985), P (Hedley and Stewart 1982) and S (Saggar et al. 1981) contents of soil microbial biomass. Literature suggests that soil microbial biomass is an integral component of SOM which regulates the storage and transformation of nutrients. Due to short turnover times (< 5 years) it is regarded as a major component of the active/labile C pool. It responds quickly to changes in land use practices, disturbance or restoration. Therefore, microbial biomass can be used in monitoring early changes in soil due to soil tillage, restoration and application of organic manures to the soil. However, the time consuming nature of microbial biomass measurement limits its usage in routine analysis. Moreover, soil microbial biomass values are affected by land use management and climate but it is difficult to compare these values across land uses, climate and soil types as no threshold values for comparison are currently available. 2.7 Conclusions With increasing concerns about global climate change, the storage and dynamics of SOC under different land uses has received more attention due to the significant potential of soils to act as a source and sink for atmospheric CO2. Ploughing and conventional seedbed preparation results in loss of SOC as CO2 from agricultural soils. Several researchers observed that adoption of No-tillage can significantly increase the SOC storage and reduce atmospheric CO2 concentrations. However, positive change in SOC caused by adoption of 46 No-tillage depends upon the soil and climatic conditions of the area. Soil texture, temperature and moisture are key factors controlling CO2 losses. Short term soil CO2 fluxes may also affected by surface pressure fluctuations caused by wind speed and this needs to be accounted for when comparing tilled and untilled soils. To date there is no standard technique to measure CO2 emissions. Different techniques have been used to measure soil CO2 emissions but chamber methods are the most common technique due to low input cost and direct measurement of CO2 fluxes. A wide range of physical, chemical and biological techniques have been applied to separate soil organic C into fractions, in an attempt to identify fractions with rapid and slow turnover rates. Several studies have been conducted to measure and characterize the young, labile fractions such as microbial biomass, light fraction, particulate organic C and the easily oxidisable fraction to relate their dynamics with various management practices. Currently, there is no standard fractionation technique to characterise a labile C fraction and develop a single relationship for a range of soils and relate it to the CO2 loss after tillage. Further research on the characterisation of labile C fractions relating to CO2 losses would be useful to evaluate the effect of land use practices. Research on the reduction of CO2 emissions with No-tillage practice in comparison to conventional tillage from agricultural soils in New Zealand is very limited. A single study conducted by Aslam et al. (2000) might not be sufficient to devise strategies to conserve soil C and reduce its loss as CO2. 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Impact of tillage and crop rotation on light fraction and intra-aggregate soil organic matter in two Oxisols. Soil & Tillage Research, 95(1-2), 196-206. 76 Chapter-3 Comparisons of methods to measure carbon dioxide emissions from soils and wind effects on soil carbon dioxide fluxes under controlled laboratory conditions 3.1 Introduction There are several techniques i.e. micrometeorological, and the use of static and dynamic chambers methods for measuring CO 2 efflux, with large differences in accuracy, spatial and temporal variability, and applicability (Lund et al. 1999). The choice of a specific technique is often a trade off between requireme nts (accuracy) and feasibility (applicability and cost). To date, the use of chambers remains the most commonly used method for measuring the emissions of greenhouse gases (Denmead 2008). According to Livingston and Hutchinson (1995) chambers are grouped into steady-state (SS) and non-steady-state (NSS) chambers, and can be further divided into non-flow-through (NFT) and flow-through (FT), depending upon the air circulation through the chamber. Based on the above classification the diff erent chamber based approaches used for measurement of CO 2 fluxes are: (1) non-flow-through- non-steady-state chamber (NFT-NSS) also known as the static chamber flux gradient method in which air samples are taken at regular intervals by syringe and subsequently analyzed by gas chromatography, (2) flow- through-non-steady-state chamber (FT-NSS) also known as a closed dy namic chamber, (3) flow-through-steady-state chamber (FT-SS) also known as an open dynamic chamber and (4) non-flow-through-steady-state chamber (NFT-SS) also known as absorption chambers or alkali trap chambers as stated by Rochette a nd Hutchinson (2005). In steady-state chambers CO 2 concentration inside the chamber remains constant and ambient and CO 2 flux is calculated using the difference in CO 2 concentration between air leaving and entering the chamber. In non-steady-state chambers, the rate of change in CO 2 concentration inside the chamber is proportional to the CO 2 flux (Livingston and Hutchinson 1995, Jensen et al. 1996, Alavoine et al. 2008). Each chamber method has its own strengths and weaknesses but, there is no single standard or reference to test the accuracy of these methods (Pumpanen et al. 2004). Past literature comparisons showed differences between chamber techniques (Rochette et al. 1992, Kabwe et al. 2002, Janssens et al. 2000, Rochette and Hutchison 77 2005); non-steady-state chambers gave lower fluxes in comparison to steady-state chambers with underestimations ranging from 4% to 35% (Rayment 2000, Pumpanen et al. 200 3, 2004). The observed differences between methods are due to a chamber?s impact on the CO 2 concentration gradient at the soil-atmosphere in terface and sensitivity to pressure differences between chamber headspace and the atmosphere. Chambers based on alkali absorp tion overestimated lower (120 mg m -2 hr-1 ) CO 2 fluxes by 25% and underestimated higher (770 mg m -2 hr-1 ) CO 2 fluxes by 57% when compared with known CO 2 fluxes (Nay et al. 199 4). Similar results were obtained by Jensen et al. (1996). They compared the static chamber alkali trap method with the closed dynamic chamber method on pasture as well as forest sites and suggested that the static method on average gave 12% higher flux rates below 100 mg CO 2 -C m -2 hr-1 , but 5 times lower flux rates above 100 mg CO 2 -C m -2 hr-1 . Therefore a comparison between chamber methods is essential if confidence with the measurement of CO 2 efflux is to be gained. The objective of this study was to compare and evaluate the three methods: 1) Static chamber using alkali traps, 2) Static chamber flux gradient method by periodic sampling and analysis of gas samples and 3) Portable Infra-Red Gas Analyser (IR GA) : EGM -1connected to SRC-1 (PP systems) for measuring a wide range of soil CO 2 emissions (Low vs. High) and variability to select the most appropriate method for measuring in-situ CO 2 fluxes. 3.2 Material and methods The chamber methods were compared on low emission sub-surface soil and high emission surface soil in this study. The experiment was conducted in a closed room without any exhaust fan to maintain a constant temperature. 3.2.1 Description of soils The soil used in this study was a Manawatu fine sandy loam (Typic Fluvial Recent Soil) under a permanent ryegrass pasture a nd had micaceous clay mineralogy (Saggar et al. 1999). Soils for the high and low emission studies were sampled from 0-6 cm and 2.5-7.5 cm depth. The reason for this was based on the root density differences reported by Kusumo (2009), that the surface soils would give 13.2 mg dry root g -1 dry soil to decompose compared with 6.1 mg dry root g -1 dry soil at 2.5-7.5 cm soil depth. For the low emission study, a sub-surface so il sample (2.5-7.5 cm depth; total C= 30 g kg -1 , total N= 3.0 g kg -1 , pH= 5.4, Olsen P= 24.6 mg kg -1 soil, exchangeable K= 249.6 mg kg -1 soil, CEC= 16 cmol c kg -1 ) was collected from one spot at 2.5-7.5cm depth avoiding the 78 dense pasture root mass contribution to readily decomposable C in autumn (March 2009). The sub-surface soil sample was passed through a 5mm sieve and stored at 20?C for 2 weeks, allowing some decomposition of the readily decomposable C. This was a strategy to produce a low emission soil. It was assumed that two weeks storage would be sufficient to decompose the readily decomposable C. Moisture content of the soil was determined on the day when soil was put into each of the six closed-base replicate PVC chambers (23.5 cm diameter; 16 cm high). Two kg of field moist sub-surface soil (22.1 % gravimetric moisture content) was transferred into each of the six re plicate chambers and firmly packed to obtain a final bulk density of 0.98 g cm -3 . Moisture content in the replicate chambers was maintained throughout the experiment by weighing the chambers and spraying the required amount of deionised water onto the surface. For the high emission study, surface soil samples (0-6.0 cm depth; total C= 32 g kg -1 , total N= 3.2 g kg -1 , pH= 5.3, Olsen P= 64.9 mg kg -1 soil, exchangeable K= 292.5 mg kg -1 soil, CEC= 17 cmol c kg -1 ) were collected from eight randomly selected areas in winter (June 2009). Three (6 cm depth and 10.5 cm diameter) core s were taken from each replicate area, sprayed with glyphosate and left in the glasshouse for 7 days to kill the surface grass. Each core was then cut into pieces and the soils from the three cores of each replicate were combined, weighed and then put in the cham bers. Moisture content of the soil was determined at this time. Each closed-base chamber was filled with 227 6 g of field moist surface soil (34.0 % gravimetric moisture conten t) and firmly packed to obtain a final bulk density of 0.86 g cm -3 . Similar amounts of field moist soil were filled in the eight chambers representing eight replicates. Moisture content did not vary much between the eight replicate chambers but differences in root masses were not accounted for. The total C content given for the surface soil is of a 2 mm sieved ring ground sample and does not take roots present in the soil into account. Moisture content in the replicate chambers was maintained throughout the experiment by weighing the chambers a nd spraying the required amount of deionised water onto the surface. 79 3.2.2 Measurement of CO2 flux Low emission study: Static alkali trap chamber method and infrared gas analyser- EGM-1were compared on the low emission sub-surface soil. 3.2.2.1 Static alkali trap chamber method : Carbon dioxide flux was determined by absorbing CO 2 using 30 ml of NaOH solution of two concen trations (0.5M and 1.0M) each in a sealed chamber headspace for a specific period of time. A plastic petri dish covering 13.1% of a chamber?s area was used for storing 30 ml of NaOH solution. The petri dish was elevated approximately 7 cm above the soil surface in the centre of the chamber. Two horizontal sticks were placed across the chamber to e levate the petri dish. For the six replicated chambers, 0.5M and 1.0M NaOH solutions were p laced respectively in three chambers each. The enclosure times were 4, 8, 12, 16, 20 and 24 hours for absorbing CO 2 from each chamber. After every absorption interval the chambers were opened, NaOH solution from each chamber was taken out and stored in a plas tic container for titration to calculate the amount of CO 2 emitted during that particular absorption interval. The amount of CO 2 emitted was further divided by the interval duration to calculate the hourly rate. The chambers were left open for 45 minutes to remove the existing CO 2 concentration built up in the chambers. Thereafter, the measurement of CO 2 flux from each chamber was taken using EGM-1. After taking the measurements with EGM-1, fresh alkali solutions were placed in each chamber for the next interval and the procedure was repea ted after every absorption interval. Different absorption intervals were not compared simultaneously as it was assumed that low emission soil will give constant CO 2 flux throughout the study period. The total amount of CO 2 absorbed in NaOH solution was determined by back-titrating the excess NaOH with 0.2M HCl after precipitation of carbonates with BaCl 2 . All the chemicals used were of analytical grade and solutions were prepared using deionised water. The solution of 0.2M HCl was standardised against Na 2 CO 3 as per the method outlined by Lambert et al. (1949). The chamber headspace volume was 5282 ml. The seal between the chamber top and its permanently installed base was perfect. E ach chamber had an internal half-turn locking system and a greased O-ring which formed a ga s-tight seal when closed with a lid. 3.2.2.2 Infrared (IR) gas analyser-Dynamic chamber method : Carbon dioxide flux was measured by a dynamic chamber (10cm diameter ; 15cm high) coupled to a portable infrared gas analyzer (IRGA) in a closed circuit (EGM -1 equipped with SRC-1, PP systems). Mixing of the air in the closed soil respiration chamber (SRC) during measurement was ensured by a small fan running inside the chamber. The fan speed was 0.5 m s -1 and there was a mesh 80 screen between the fan and the soil surface to slow the air velocity at the soil surface. The mixed air was circulated (flow rate 200 -400 ml min -1 ) from the chamber into the IRGA sensor cell and back to the chamber by a pump in the EGM-1. The IRGA contained software to calculate CO 2 flux rates and each measurement took less than 2 minutes (PP systems 2010). Before each flux measurement, the SRC was moved away from the closed base chamber filled with the soil and the SRC mixing fan ran for 10s to restore the IRGA sensor cell to the ambient CO 2 concentration in the room. Three CO 2 flux measurements with EGM-1 were made from three different locations within each chamber at the end of every absorption interval i.e. 4, 8, 12, 16, 20, 24 hours of the alkali trap method. The mean of three flux values are stated as the flux value from each replicate chamber. In this study a soil collar for the placement and formation of a seal for the dynamic chamber was not used. High emission study: Static alkali trap chamber, infrared gas analyser-EGM-1 and static chamber flux gradient methods were co mpared on high emission surface soils. Carbon dioxide measurements were made 2 hours after filling the chambers with soil. 3.2.2.3 Static chamber flux gradient method : Carbon dioxide gas samples (25 ml) were taken for a period of one hour with 60 ml polypropylene syringes fitted with 3-way stopcocks after sealing the chambers with a lid having one port. Five gas samples (25 ml each) were taken from each chamber at times t0, t10, t20, t30 and t60 (i.e. 0 min, 10 min, 20 min, 30 min and 60 min respectively, after closing the chamber). The gas samples were transferred to 12 ml evacuated vials and then analyzed using a Shimadzu GC- 17A gas chromatograph; CO 2 flux (mg m -2 hr-1 ) was estimated from the measurements made at different time intervals. The sample of the ambient air taken just after clos ing the chamber (t0) was used as a reference for calculating CO 2 fluxes. Carbon dioxide gas samples (25 ml) were taken by syringe from all the eight replicate chambers. After completion of a one hour measurement period, chambers were left open for 45 minutes to remove the existing CO 2 concentration built up in the chambers. Thereafter, 1.0 M NaOH traps we re placed in the chambers to absorb CO 2 for 4 hours. The seal between the chamber top and it s permanently installed base was perfect. Each chamber had an internal half-turn lockin g system and a greased O-ring which formed a gas-tight seal when closed with a lid. The chamber headspace volume was 4937 ml. 3.2.2.4 Static chamber alkali trap method : Carbon dioxide flux rates were determined by absorbing CO 2 in 1.0M NaOH placed in a plastic pe tri dish (13.1% of the chamber area) in all the eight replicates for 4 hours duration. 81 3.2.2.5 Infrared (IR) gas analyser-Dynamic chamber method : Three measurements from different locations within each chamber were made at the end of a 4 hour absorption interval, as for the alkali trap method. 3.2.3 Statistical analysis An analysis of variance using SAS software (9.1) was performed on carbon dioxide fluxes measured by the three methods using the General Linear Model (GLM) procedure. Mean comparisons between CO 2 fluxes were done using Fisher?s least significant difference (LSD ) at 5% level of significance. 3.3 Results 3.3.1 Low emission study Two chamber methods (static chamber (alkal i trap) and dynamic chamber (EGM -1) ) and two NaOH concentrations (0.5M and 1.0M ) were compared on the low emission sub- surface soil under controlled laboratory conditions. The CO 2 fluxes measured by 0.5M NaOH traps varied from 43.5 mg CO 2 m -2 hr-1 to 55.6 mg CO 2 m -2 hr-1 , and by 1.0M NaOH traps varied from 52.4 mg CO 2 m -2 hr-1 to 69.9 mg CO 2 m -2 hr-1 (Table 3.1). The CO 2 fluxes measured by 0.5M and 1.0M NaOH traps over a 12 hour absorption interval were significantly higher than those measured over 4 and 8 hour intervals. Table 3.1: Effect of absorption intervals on the CO 2 fluxes using closed static chambers with NaOH (alkali) traps of varying molarities. Absorption interval (hours) CO 2 flux (mg CO 2 m -2 hr-1 ) 0.5M NaOH 1.0M NaOH 4 B 43.5?5.1 a B 61.4?4.4 a 8 B 44.8?2.5 a C 52.4?1.2 a 12 A 55.4?2.2 a A 69.9?2.2 b 16 A 55.6?2.2 a BC 57.6?1.1 a 20 BA 50.1?1.0 a C 53.2?0.5 a 24 BA 48.6?0.7 a C 54.1?0.8 b Each value represents a mean of three replicates? SE Capital letters represents absorption interval difference Small letters represents absorption NaOH concentration difference Means followed by same small letter in rows and sa me letter in columns are not significantly different The CO 2 fluxes measured over a 20 and 24 hour absorption interval by both 0.5M and 1.0M NaOH trap solutions were lower than those measured over a 12 hour interval; however, significant differences were observed only in the case of 1.0M NaOH. No 82 significant difference was observed between the CO 2 fluxes measured by 0.5M and 1.0M NaOH except over the 12 and 24 hour intervals . The CO 2 fluxes measured by EGM-1 equipped with SRC-1 in the chambers with 0.5M NaOH traps varied from 45.5 mg CO 2 m -2 hr-1 to 103.3 mg CO 2 m -2 hr-1 , and in the chambers with 1.0M NaOH traps, varied from 43.3 mg CO 2 m -2 hr-1 to 91.1 mg CO 2 m -2 hr-1 (Tables 3.2 a, b). The CO 2 fluxes measured by EGM-1 over a 24 hour absorption interval (both in 0.5M and 1.0M NaOH chambers) were significantly higher than any other absorption interval. Table 3.2: Effect of absorption intervals on CO 2 fluxes using closed static chambers with NaOH (alkali) traps 0.5M NaOH (A) and 1.0M NaOH (B) compared with spot measurements made by EGM-1 equipped with SRC-1. (A) Absorption interval (hours) CO 2 flux (mg CO 2 m -2 hr-1 ) 0.5M NaOH EGM-1 4 B 43.5?5.1 a B 45.5?4.0 a 8 B 44.8?2.5 a B 56.6?10.7 a 12 A 55.4?2.2 a B 50.0?5.7 a 16 A 55.6?2.2 a B 53.3?1.9 a 20 BA 50.1?1.0 a A 98.9?12.8 b 24 BA 48.6?0.7 a A 103.3?12.6 b (B) Absorption interval (hours) CO 2 flux (mg CO 2 m -2 hr-1 ) 1.0M NaOH EGM-1 4 B 61.4?4.4 a B 54.4?4.4 a 8 C 52.4?1.2 a B 43.3?6.6 a 12 A 69.9?2.2 a B 46.6?3.3 b 16 BC 57.6?1.1 a B 60.0?16.7 a 20 C 53.2?0.5 a B 66.6?3.8 b 24 C 54.1?0.8 a A 91.1?2.2 b Each value represents a mean of three replicates? SE Capital letters represents absorption interval difference Small letters represents NaOH and EGM -1 method difference Means followed by same small letter in rows and sa me letter in columns are not significantly different A comparison between the CO 2 fluxes as measured by alkali traps (0.5M and 1.0M NaOH) with EGM-1 (Tables 3.2 a, b) shows similar CO 2 fluxes for the majority of the 83 absorption intervals. However, CO 2 fluxes measured by EGM-1 at 20 and 24 hour absorption intervals were significantly higher than for the alkali trap method. In the present study the amount of residual alkali after a 24 hour absorption interval was 84.0% for 0.5M and 91.0% for 1.0M NaOH. Kirita and Hozumi (1966) stated that to obtain maximum absorption of CO 2 the amount of unused alkali should not be less than 80% , which suggested that both concentrations were capable of maximum CO 2 absorption but 1.0M NaOH had more capacity to absorb the higher CO 2 fluxes. Therefore, in the high emissions study 1.0M NaOH was used to measure CO 2 fluxes. 3.3.2 High emission study Three chamber methods were investigated: static chamber alkali trap using 1.0M NaOH, dynamic chamber (EGM -1equipped with SRC-1), and the static chamber flux gradient method were compared on high emission surface soil under controlled laboratory conditions. Carbon dioxide fluxes for high emission study were measured for six weeks on days 1, 3, 5, 8, 10, 12, 15, 17, 19, 36, 38 and 40. The CO 2 fluxes measured by 1.0M NaOH traps varied from 553.5 to 1166.5 mg CO 2 m -2 hr-1 , by EGM -1 they varied from 118.2 to 956.6 mg CO 2 m -2 hr-1 and by the static chamber flux gradient method they varied from 79.5 to 613.3 mg CO 2 m -2 hr-1 over the six week period (Figure 3.1). The patterns of CO 2 fluxes were similar for the three measurement methods; higher fluxes were observed in the beginning and these then decreased gradually. The CO 2 fluxes measured by a 1.0M NaOH trap, EGM-1, and the closed chamber flux gradient method on any measurement day varied significantly from each other. CO 2 flux measurements by a 1.0M NaOH trap were significantly higher than the EGM-1 and the flux gradient method. The average absolute differences between the alkali trap and EGM- 1measured fluxes during the first, second, third and sixth week were 264.6, 289.2, 348.0 and 439.6 mg CO 2 m -2 hr-1 , which suggested that on average, the alkali trap method gave 30%, 85.4%, 121.1%, and 360 % higher fluxes than the EGM-1. Similarly, the average absolute differences between the alkali trap and flux gradient measured fluxes during the first, second, third and sixth week were 564.8, 467.1, 500.4, 472.3 mg CO 2 m -2 hr-1 suggesting 96.6%, 262.7%, 368.8% and 549.5% higher fluxes in the fi rst, second, third and sixth week by the alkali trap method than by the closed static flux gradient method. 84 Figure.3.1: Comparison of CO 2 fluxes measured by 1.0M NaOH traps, EGM-1 and closed static flux gradient methods. Each value represents the mean of eight replicates with standard errors (?) shown by vertical bars. Vertical bars above represent LS D (P < 0.05) for comparison among the three methods where significant differences were found. 3.4 Discussion 3.4.1 Low emission study Two major chamber approaches: the closed static chamber with an alkali trap and the closed dynamic chamber system (EGM-1) were compared on low emission sub-surface soil. The alkali trap provides integrated flux rates over time. Most of the alkali trap (concentration of NaOH varies from 0.5M to 2M depe nding upon the expected flux values) CO 2 flux values reported in the literature were obtained under long absorption times i.e. 24 hours (Buyanovsky et al. 198 6; Rochette et al. 1992 ; Jensen et al. 1996; Aslam et al. 2000; Alvarez et al. 2001; Yim et al. 2002; Rottmann and Joergensen 2011). However, a long absorption time of 24 hours disturbs the natural conditions of the enclosed soil surface and provides either under- and over-estimation of CO 2 fluxes (Davidson et al. 2002). The present study verified that CO 2 fluxes measured by 1.0M NaOH over longer absorption intervals of 20 and 24 hours were significantly lower than the CO 2 fluxes measured over short absorption intervals of 4 and 12 hours. During long absorption intervals, the absorption of CO 2 by the NaOH (alkali) trap is limiting, resulting in a build-up of CO 2 concentration inside the chamber (Jensen et al. 1996). In alkali trap absorption, CO 2 is first absorbed at the liquid 0 30 0 60 0 90 0 12 00 15 00 0 5 10 15 20 25 30 35 40 45 mg CO 2 m- 2 hr -1 Days Na OH trap E GM - 1 F lu x Gra die nt 85 interface and then diffuses into the bulk solution. The NaOH solution placed in the chamber was not stirred, which may have resulted in a carbonate gradient which limited the capacity of NaOH to absorb CO 2 . Moreover, long absorption intervals causes oxygen depletion, the build-up of CO 2 concentration and change in temperature & moisture content inside chambers, leading to low flux rates (Norman et al. 1997; Rochette and Hutchinson 2005). In the present study, CO 2 fluxes measured by 1.0M NaOH were similar to the fluxes measured by 0.5M NaOH during the majority of absorption intervals, except for fluxes measured over 12 and 24 hour intervals. Carbon dioxide fluxes measured by 1.0M NaOH were significantly higher than those measured by 0.5M NaOH at 12 and 24 hour absorption intervals. There seems to be no effect of alkali concentration on CO 2 fluxes; any differences that occurred over 12 and 24 hour intervals were likely due to experimental variability. Comparison of CO 2 fluxes measured by alkali traps (0.5M and 1.0M NaOH) and EGM-1 showed no differences for the majority of the absorption intervals except for the 20 and 24 hour intervals where fluxes measured by EGM-1were significantly higher than those of the alkali trap method. Higher CO 2 concentration build-up inside the chambers due to long absorption intervals of 20 and 24 hours could explain why significantly higher fluxes were measured by EGM-1 at 20 and 24 hours absorption intervals. The EGM-1 measurements were made on the chambers 45 minutes after re moving the NaOH (alkali) traps. EGM-1 measured fluxes were actually the flush of CO 2 that was built-up inside the chambers due to the long absorption intervals. Davidson et al. (2002) suggested that short sampling duration of 2 minutes by EGM-1 minimizes the artefact caused by altering the CO 2 concentration gradient between the soil atmosphere and the chamber headspace. However, the spot measurements made by EGM-1 showed greater variability than those made from alkali traps. The static alkali trap chamber method is the most commonly used method to measure soil CO 2 efflux (Yim et al. 2002). However, this method is greatly influenced by numerous methodological factors such as solution strengths, volumes, absorption areas and absorption times that in turn can affect the flux rates. G upta and Singh (1977) compared different NaOH (alkali) concentrations (0.1M, 0.25M, 0.5M. 0.75M, 1.0M, 1.25M) under field conditions. The volume of alkali was kept at 10ml in each case and the alkali trap/chamber area ratio was 12.5%. They observed a linear increase in the amount of CO 2 absorbed with increasing concentration of NaOH from 0.1M (45 ? 1.8 mg CO 2 m -2 hr-1 ) to 1.25M (282 ? 5.9 mg CO 2 m-2 hr-1 ), showing that increase in concentration of NaOH can affect the calculated flux with this technique. However, in a separate expe riment using different volumes (10, 20, 30, 40 and 50 ml) and concentrations (0.1-1.25M) of NaOH under field conditions, Gupta and 86 Singh (1977) showed that additional increase in NaOH volume beyond 30ml had no effect on CO 2 fluxes at 0.75, 1.0 and 1.25M measured for a 24 hour interval. Similarly, Minderman and Vulto (1973) using different alkali (KOH ) concentrations under field conditions (0.25- 2.0M) showed that additional increase in alkali volume beyond 30ml had no effect on CO 2 fluxes as measured for a 24 hour interval. The size and shape of the alkali container is also important as it determines the ratio of the alka li surface area to ground enclosed by the chamber. For efficient CO 2 absorption, Kirita (1971) recommended a 13% alkali/chamber area ratio depending on their chamber designs and deployment conditions. The appropriate alkali (NaOH/KOH) concentration depends upon the ecosystem and expected CO 2 fluxes. The past literature and results of the present study suggest the use of a short absorption period of 4 hours for measuring CO 2 fluxes to minimize the chamber effects on CO 2 fluxes. 3.4.2 High emission study In arable ecosystems flux rates obtained by the static chamber method have ranged between 0 and 1100 mg CO 2 m -2 hr-1 (Dao 1998; Aslam et al. 2000; Alvarez et al. 2001; Omonode et al. 2007; Ussiri and Lal 2009) and by the dynamic chamber method from 0 to 4500 mg CO 2 m -2 hr-1 (Reicosky and Lindstrom 1993; Reicosky et al. 199 7; Alvaro-Fuentes et al. 2007; Bauer et al. 2006; Akbolat et al. 2009). The CO 2 fluxes measured in the high emission study by the static and dynamic methods are within the range reported for arable ecosystems. The patterns of CO 2 fluxes measured during the high emission study were similar for the three measurement methods; higher fluxes were observed in the beginning and these then decreased gradually. This suggested an initial fast phase due to mineralization of easily degradable organic C in residues, followed by a slower phase in which more complex and recalcitrant forms or transformed metabolites are mineralized (Verma et al. 2010). The soil moisture and temperature were constant throughout the measurement period. The results of a high emission study contradicted the findings of the previous comparative studies in which CO 2 flux estimates from the alkali traps were much lower than those from the EGM-1 and other dynamic methods (Ewel et al. 1987; Rochette et al. 1992). The lower fluxes obtained with EGM-1 compared to alkali trap fluxes in this study may either be caused by systematic under-recording due to poor sealing with the soil surface in the chamber leading to air leakage, or the NaOH trap method might be over-estimating the CO 2 fluxes. The flux rates determined by EGM - 1 are also influenced by the number and frequency of measurements (King and Harrison 2002 ). In the present study CO 2 flux 87 measurements with EGM-1 were made once during the day of measurement. More measurements at different time intervals would have improved the accuracy and comparability with alkali trap measurements. The smaller flux values measured by the closed static chamber flux gradient method might have resulted from the reduced soil-atmospheric exchange of CO 2 due to concentration build up within the chamber (Conen and Smith 2000). Nakadai et al. (1993) observed acceleration in soil CO 2 fluxes with decreasing CO 2 concentration in the chamber using the alkali trap static chamber method under laboratory conditions. In order to check whether the al kali trap (1.0M NaOH) was overestimating CO 2 fluxes, plastic petri dishes containing 30 ml of 1.0M of NaOH (13.1% of the chamber area) were placed in four replicate chambers to absorb CO 2 and simultaneous measurements of gas concentrations were done by syringe sampling. Each chamber had an internal half-turn locking system and a greased O-ring which formed a gas-tight seal when closed with a lid. Eight gas samples were taken at t0, t10, t20, t3 0, t60, t120, t180 and t240 (i.e. 0 min, 10 min, 20 min, 30 min, 60 min, 120 min, 180 min and 240 min, respectively from each chamber after closing the chamber with 1.0M NaOH trap). The chambers were sealed with air tight lids. The amount of increase and decrease in CO 2 concentration in the chamber headspace depends upon the efficiency of CO 2 absorption by the alkali trap (Bekku et al. 1997). The CO 2 concentration in the chambers decreased with time showing that the NaOH solution in the chamber was efficiently absorbing the CO 2 released from the soil. Mean head space CO 2 concentration in the four chambers decreased from 641 ppm at t0 to 35 3 ppm at t240 showing that the CO 2 concentration in the chambers did not go much below the ambient atmospheric CO 2 concentration during the four hour absorption period, thus overruling any overestimation of CO 2 flux by the alkali trap. When the headspace CO 2 concentration is equal to the ambient level, the absorption rate is equal to the surface flux (Hutchinson and Rochette 2003). The ambient CO 2 concentration of the room was 612 ppm. The ambient concentration of CO 2 was high because the experiment was conducted in a closed room without any exhaust fan to maintain the constant temperature; Nakadai et al. (1993) observed approximately 500 ppm CO 2 initial concentration under laboratory conditions using the alkali absorption method. The ideal method for measuring CO 2 flux should have minimal influence on soil surface natural conditions and the concentration gradient, hence giving a reliable estimate of CO 2 efflux (Rochette and Hutchinson 2005). Theoretically, the dynamic method (EGM -1 ) fulfils this condition to a greater extent than the static method, with measurements over a short period of time minimizing chamber effe cts on soil temperature, moisture and the CO 2 88 concentration gradient (Le Dantec et al. 1999; Janssens et al. 2000). However, the dynamic method does not have the time-integrative pow er of the static method unless a very large number of measurements are taken during the measurement period. Such high daily sampling frequency poses c onstraints and is not easily accomplished with manual measurements (Jensen et al. 1996; King and Harrison 2002). This was the key reason why the alkali trap method was preferred over EGM -1. The flux gradient method was sensitive to large increases in the concentration of emitted CO 2 and expensive compared to the alkali trap method. Hence the alkali trap method was preferred over EGM-1 and the flux-gradient method to measure CO 2 fluxes in further studies. 3.5 Summary and conclusions The flux of CO 2 emissions from soil may be needed in a suite of measurements required for monitoring short-term management effects on changes in soil C stocks. The three different chamber methods: i) static alkali tr aps, ii) flux gradient and iii) a portable infra-red gas analyser (IRGA): EGM-1connected to SRC-1 (PP systems) were compared for measuring CO 2 fluxes under controlled laboratory c onditions using low (sub-surface) and high (surface) CO 2 -emitting soils, using a range of enclosure intervals (4, 8, 12, 16, 20 and 24 hours). Carbon dioxide fluxes measured by alkali trap and EGM -1 in the low emission study were similar during the majority of enclosure intervals except at 20 and 24 hours where the alkali traps measured significantly lower fluxes than EGM - 1. This could be explained by the build-up of CO 2 concentrations inside the chamber as being closed for 20 and 24 hours affects measured CO 2 fluxes because soil flux rates depend upon the concentration gradient inside the chamber. However, the fluxes measured over a six week period from the high emitting surface soil ranged between 553.5 and 1166.5 mg CO 2 m -2 hr-1 for the NaOH traps, between 118.2 and 956.6 mg CO 2 m -2 hr-1 for the EGM -1 and between 79.5 and 613.3 mg CO 2 m -2 hr-1 for the flux gradient method. The lower flux values obtained with EGM -1 and the flux gradient method compared to alkali traps may be caused by systematic under-recording. Poor sealing of the EGM-1 with the soil surface, and decreasing atmospheric/soil CO 2 gradients under the flux gradient technique were suspected reasons. Overall results of this study show that EGM - 1 measurements were highly variable in comparison to other methods. For extended periods of monitoring this method requires numerous replicated spot measurements. Thus, the measurement frequency with EGM-1 poses constraints and may not be easily accomplished. 89 The static chamber flux gradient method seems sens itive to rapid and large increases in the concentration of emitted CO 2 and expensive compared to the alkali trap method. By contrast, the alkali trap method was not sensitive to different rates of CO 2 emission and has the advantages of simplicity, cheapness and integrating CO 2 flux over time (4 to 24 hours). Therefore, the alkali trap method was preferre d over EGM-1 and the flux-gradient method to measure CO 2 fluxes in the following laboratory and field studies. 90 3.6 Effect of wind induced pressure fluctuations on carbon dioxide fluxes from simulated tillage and No-tillage treatments 3.6.1 Introduction Generally, higher soil CO 2 fluxes are expected under high wind velocity due to the lower (negative) pressure created at the soil surface (Kimball and Lemon 1971 ; Takle et al. 2004; Xu et al. 2006) which pulls the CO 2 rich air from the soil. This phenomenon is known as the ?Venturi Effect ? (Conen and Smith 1998; Reicosky and Archer 2007). However, pressure fluctuations only affect the CO 2 transport and not its production which suggests that the venturi effect may not have significant long term effect on CO 2 emissions from soils (Subke et al. 2003). In this section I report the result s of the pressure fluctuation study conducted under controlled laboratory conditions comparing the differences in CO 2 fluxes from simulated tilled and No-tilled soils ma intained at ambient and lower pressure conditions. 3.6.2 Material and methods 3.6.2.1 Soil characteristics The experiment was conducted on Ohakea si lt loam soil (0-10 cm) having 17% sand, 59% silt and 24% clay with the following char acteristics: pH 5.9 (1:2.5 soil:water); bulk density of undisturbed soil was 1.07 g cm -3 ; total C & N contents were 39.7 g kg -1 & 3.5 g kg -1 , Olsen P: 21.5 mg kg -1 soil, exchangeable K: 163.8 mg kg -1 soil, SO 4 -S: 8.25 mg kg -1 soil and CEC 19.8 cmol c kg -1 ; gravimetric soil water content at the time of sampling was 44.8 percent. 3.6.2.2 Chemical and physical analysis Soils samples for chemical and physical analys is were collected in triplicate (0-10 cm soil depth) on the same day as soil cores were collected for CO 2 measurements. Each replicate was composed of 12 cores (2.5 cm di ameter) randomly collected across the field. Soil samples were air dried a nd ground to pass through a 2mm sieve for further analysis. Soil pH (1:2.5), cation exchange capacity (CEC) and ex changeable K were analysed according to Blackmore et al. (1987). Olsen P and SO 4 -S were determined according to Olsen et al. (1954) and Blackmore et al. (1987). A portion of each sample was ground to pass through a 0.2 mm sieve for total carbon and nitrogen analysis using a LECO induction furnace (Blackmore et al. 1987). Particle size distribution wa s determined by the pipette method of Claydon (1989). For soil bulk density determina tion, undisturbed soil cores (10 cm diameter, 91 10 cm depth) were taken and soil bulk densit y was calculated by dividing the mass of oven dried soil by the volume of the soil core. The gravimetric soil moisture content (SMC) was measured at 0-10 cm depth. Field mois t samples were weighed, oven-dried (105 oC) to constant mass, and weighed again. The final mass ( Ms ), and the difference between the field moist and dry masses ( Mw ), were used to calculate the gravimetric SMC = ( Mw/Ms ) X 100. 3.6.2.3 Collection of soil cores and simulation of tillage A total of fifty four in-situ soil cores (10 cm diameter; 10 cm depth) were collected from the Sanson site (40 ?14'34.33 "S and 175?21 '27.07"E) during winter for simulated tillage (ST), No-tillage (NT) and non-disturbed (ND) co ntrol treatments. The site was sprayed with glyphosate herbicide prior to collection of the soil cores. Thirty six ND cores were collected after glyphosate application and before the start of tillage operations from three different locations on the farm, with twelve cores taken from each location. Immediately after the Cross Slot ? No-tillage drilling eighteen NT cores were collected over the slots, with six cores collected from each location. Eighteen of the thirty six ND cores were broken up in the laboratory to simulate tillage treatment. The ST treatment was formulated from three in-situ ND soil cores by emptying the soil, breaking it into pieces, thoroughly mixing and packing it in a plastic container (19 cm diameter; 10 cm high) to obtain a final bulk density of 0.63 g cm-3 . Each replicate of ST, NT and ND treatments were comp rised of three intact cores collected from one sampling location. To calculate the CO 2 flux from the three treatments the amount of CO 2 emitted from each chamber was divided by the combined surface area of the three cores as the surface area of the plastic container used for the ST was comparable to the combined surface area of the three cores. Three replicates of each tillage treatment (ST, NT and ND) with no soil control were assessed both at ambient and lower pressures. 3.6.2.4 Application of different pressures to soils The lower (negative) pressure of 20 millib ars was obtained in each replicate chamber by using an individual bubble tower (Plate 3.1 A). A bubble tower column was constructed from a plastic tube (diameter=3.0cm; height=45 .0cm) and sealed at the top by a two-hole rubber stopper. One hole was for the inlet t ube (diameter=6.0mm; height=500mm) and the other hole for the outlet tube to the chamber. The lower end of the column also had a rubber stopper. All the connections were made with appropriate PVC tubing of 4.0 mm diameter. The bubble tower column was filled up to 35 cm with water and lower pressure was adjusted by lowering the inlet tube 20 cm below the surf ace of the water in the column. The flow of air was maintained constant in each replicate chamber by adjusting the bubble rates to approximately three bubbles per second (a pproximately a flow rate of 30 ml min -1 allowing 92 chamber headspace turnover in 4 hours) in th e bubble tower column and the exhaust gas scrubbing glass tube containing 50ml 1.0M NaOH solution. For ambient pressure, chambers were set up identically to those set up at lower pressure but without a bubble column (Plate 3.1 B). The air flow was also adjusted to a bubbling rate approximately three bubbles per sec ond in the exhaust gas scrubbing glass tube containing 50ml 1.0M NaOH solution so that equal air flows were obtained for ambient and lower pressure chambers. 3.6.2.5 Carbon dioxide measurements Carbon dioxide was measured using the alkali trap static chamber method. Carbon dioxide measurements started on the same day of sampling, immediately after formulation of the simulated tillage treatment. Soil cores and containers filled with soil were placed in closed base static chambers along with a plastic petri dish containing 30 ml 1.0M NaOH to absorb CO 2 inside the chambers. Each chamber was sealed with an air tight lid having two ports. One port was connected to the bubble towe r column and the other port was connected to the glass tube containing 50ml 1.0M NaOH solution sealed at the top with a two-hole rubber stopper. One hole was for the inlet t ube (diameter=6.0mm; height=200mm) which was dipped in NaOH solution connected to the st atic chamber, and the other hole was for the short plastic tube which was connected to the suction hose. All the connections were made with appropriate PVC tubing of 4.0 mm diameter. Carbon dioxide was absorbed for a duration of 16 hours which was split into 4 and 12 hour measurement periods. After completion of the 4 hour measurement period, NaOH solutions were immediately replaced with fresh NaOH solu tions and then measurements were taken for another 12 hour period. One measurement of 16 hours was made in the first week after sampling of the soil cores and another measurement of 16 hours was made in the second week after sampling of the soil cores for all three (ST, NT and ND) treatments. The amounts of CO 2 absorbed in the NaOH solutions were determined by titrating them with dilute 0.2M HCl. The flux values were calculated by dividing the amounts of CO 2 absorbed by NaOH solutions by the measurement periods. The flux values calculated from the amounts of CO 2 absorbed by NaOH solutions in a p lastic petri dish and glass tube were summed up to report as a lone flux value at a particular measurement period and pressure. Moisture contents in the soil cores and c ontainer were maintained throughout the experiment by weighing the soil cores and container and spraying the required amount of deionised water onto the surface to bring them to their original moisture at which they were sampled from the field. 93 A B Plate.3.1: Closed static chambers: lower pressure setup (A) and ambient pressure setup (B). 3.6.2.6 Statistical analysis Mean carbon dioxide fluxes from the three tillag e treatments at ambient and lower pressures were compared using the Kruskal-Wallis test followed by the Bonferroni (Dunn) t-test. 3.6.3 Results Carbon dioxide fluxes measured over 4 and 12 hours under ambient and lower pressures during the two week study period are given in Table 3.3. Measurements made during the first week after sampling of soil cores suggest that CO 2 -C fluxes for ST, NT and ND treatments with the onset of lower (negativ e) pressure were significantly higher than CO 2 -C fluxes at ambient pressure by 12.0, 14 .5, and 33.8%, respectively for a 4 hour measurement period. Thereafter, significantly higher CO 2 -C fluxes were observed under lower pressure by 4.6 and 17.2 % for NT and ND treatments for the following 12 hour measurement period. No significant differences were observed between CO 2 -C fluxes under ambient and lower pressures for the ST treatment during the 12 hour measurement period (Table 3.3). During the second week, no signif icant differences were observed between CO 2 - C fluxes under ambient and lower pressures for the ST, NT and ND treatments for a 4 hour measurement period. However, during the 12 hour measurement period significant differences between CO 2 fluxes under ambient and lower pre ssures were observed for the NT and ND treatments. At ambient pressure CO 2 -C fluxes from ST were significantly higher than the NT treatment only for a 4 hour mea surement period during the first week. Inlet tube C olum n Outlet tube Glass tube with NaOH 94 Thereafter, no significant differences between th e three treatments were found at subsequent measurements. Under lower pressure, CO 2 -C fluxes from the ST treatment during any measurement interval during the two week study period were not significantly higher than the NT treatment. However, CO 2 -C fluxes from the ND treatment were consistently higher than the NT and ST treatments throughout th e study period. Similar trends and statistical results were observed for the accumulated CO 2 -C emissions over 4 and 12 hour measurement periods for the whole study duration (Table 3.4). The accumulated CO 2 -C emissions over 16 hour measurement period for the whole study duration suggested that irrespective of the three (ST, NT and ND) tillage treatments, higher CO 2 -C emissions were measured with the onset of lower pressure than those measured under ambient pressure (Table 3.4). 95 Table 3.3: Carbon dioxide fluxes (mg CO 2 -C m -2 hr-1 ) under lower (negative) and ambient pressures in laboratory conditions during 4 and 12 hour measurement period. First week mg CO 2 -C m -2 hr-1 4 hours ST NT ND Lower pressure A 358?9.8 a b A 309?2.9 b A 380?9.5 a Ambient pressure B 3 20 ? 7.9 a B 270? 1 2.0 b B 284? 1 1.0 a b mg CO 2 -C m -2 hr-1 12 hours ST NT ND Lower pressure A 297?1 1.8 a b A 273?2.7 b A 339?2.3 a Ambient pressure A 290?1 5.1 a B 2 61 ? 2.5 a B 2 90 ? 2.4 a Second week mg CO 2 -C m -2 hr-1 4 hours ST NT ND Lower pressure A 226?1 9.4 a A 200 ?6.2 a A 226?1 0.5 a Ambient pressure A 2 0 2 ?1 1.2 a A 1 8 7 ?5.0 a A 2 0 4 ?2.4 a mg CO 2 -C m -2 hr-1 12 hours ST NT ND Lower pressure A 219?9.0 b A 2 2 6 ?1 0.0 a b A 280?4.8 a Ambient pressure A 222?1 1.1 a B 208 ? 0.8 a B 2 51 ? 5.3 a Table 3.4: Accumulated carbon dioxide emissions (mg CO 2 -C m -2 ) over 4, 12 and 16 hour measurement period under lower (negative) and ambient pressures in laboratory conditions. First week mg CO 2 -C m -2 4 hours ST NT ND Lower pressure A 143 3? 3 9.3 a b A 123 5? 1 1.5 b A 152 2? 3 8.2 a Ambient pressure B 1280 ? 31.7 a B 1079 ? 48.0 b B 1137 ? 44.2 a b mg CO 2 -C m -2 12 hours ST NT ND Lower pressure A 3558? 1 41.1 a b A 327 2? 3 2.5 b A 407 2? 2 7.2 a Ambient pressure A 3477? 1 81.7 a B 3 12 7 ? 30.5 a B 3474 ? 28.3 a mg CO 2 -C m -2 16 hours ST NT ND Lower pressure A 4991? 1 06.1 a A 450 7? 4 1.7 b A 559 4? 2 5.5 c Ambient pressure A 4757? 2 04.6 a B 4207 ? 78.5 a B 4610 ? 70.1 a Second week mg CO 2 -C m -2 4 hours ST NT ND Lower pressure A 904?7 7.7 a A 799?2 4.6 a A 902?4 2.1 a Ambient pressure A 809?4 4.7 a A 7 4 9 ?1 9.8 a A 815?9.6 a mg CO 2 -C m -2 12 hours ST NT ND Lower pressure A 2633? 1 08.4 b A 271 5? 2 0.5 a b A 335 5? 5 7.4 a Ambient pressure A 2 6 7 0? 1 33.2 a B 249 1 ? 9.5 b B 3012 ? 63.5 a mg CO 2 -C m -2 16 hours ST NT ND Lower pressure A 3537? 1 84.5 a A 3514? 1 07.1 a A 425 7? 7 9.3 a Ambient pressure A 3479? 1 77.8 a B 3240 ? 23.0 a B 3827 ? 72.9 a Each value in the table represents the mean of three replicates with standard errors (?). Capital letters represent pressure difference Small letters represent tillage treatment difference Means ( ?S.E) followed by the same small letter in rows and same capital letter in column for the tested time intervals are not significantly different. 96 3.6.4 Discussion Soil CO 2 flux measurements are very sensitive to pressure fluctuations (Fang and Moncrieff 1998). Most of the past studies re ported how pressure differences between the outside and inside of the chamber (created due to air flow rate through the chamber and wind velocity) affects the soil CO 2 efflux (Hanson et al. 1993; Lund et al. 1999; Longdoz et al. 2000; Welles et al. 2 0 0 1 ; Suleau et al. 2009 ). Results of the present study support the general perception that higher CO 2 -C fluxes are observed under lower pressures due to high wind velocity. Irrespective of the three (ST, NT and ND) tillage treatments higher CO 2 -C fluxes were measured with the onset of lower pressure than under ambient pressure throughout the two week study period. A physical explanation of this could be th at while a negative pressure gradient exists between air in the chamber and air contained in soil pores, soil air rich in CO 2 moves to equilibrate the pressure gr adient. Also the lower CO 2 partial pressure in the chamber will result in a greater CO 2 diffusion gradient leading to higher initial fluxes. For the ST treatment significantly higher CO 2 -C fluxes under lower pressure were observed only for the four hour measurement period during the first week. Thereafter, no significant differences were found between the CO 2 -C fluxes at ambient and lower pressures. Loosening of soil resulted in the rapid decline in CO 2 concentration in the soil pores resulting in higher CO 2 -C fluxes only for the four hour measurement period . Similarly, in a field experiment, Reicosky et al. (2008) observed a rapid drop in the CO 2 concentration in a ploughed soil surface due to wind induced lower pressures and suggested the change in soil porosity following tillage results in rapid gas exchange leading to lower CO 2 concentrations in ploughed soils. Absence of a significant effect of lower pressure for the ST treatment during any other measurement period except for the four hour measurement period during the first week suggests that pressure fluctuations affect only the CO 2 transport but not production. In this experiment measurements were taken for 4 hour and 12 hour durations and no attempts were made to measure these losses on an hourly basis. Subke et al. ( 2 0 0 3 ) measured CO 2 fluxes in field conditions using an open dynamic chamber with an aim to model long term CO 2 fluxes, and emphasized that there was no need to include the effect of pressure fluctuations, as it affects only transport but not production. Moreover, averaged over time the net contributi on of pressure related transport is zero as high fluxes due to negative pressure are follo wed by low fluxes due to positive pressure. Slow but continuous gas-exchange from the NT and ND treatments due to higher bulk 97 densities and greater tortuosity of pore space in comparison to the ST treatment resulted in higher CO 2 -C fluxes under lower pressure. Result s suggest that the effect of pressure fluctuations on soil CO 2 fluxes differ due to soil proper ties such as soil bulk density and porosity, with observed effects lasting for two weeks on NT and ND treatments. 3.6.5 Summary and conclusions To better understand the impact of pressure fluctuations caused by wind velocity on soil CO 2 emissions from simulated tillage (ST) a nd No-tillage (NT) treatments, a two-week laboratory study was conducted. Carbon dioxide flux measurements were made using the alkali trap method from simulated tilled (ST) soils and No-tillage (NT) in-situ soil cores simultaneously maintained at ambient and lower pressures (a negative pressure of 20 millibars was created using an individual bubble tower for each chamber). Lower pressure had a significant effect on CO 2 -C fluxes from the ST treatment only for the 4 hour measurement period of the first week. Thereafter, no significant differences between the two pressure treatments were observed. Tillage induced change in soil air porosity resulted in immediate exchange of CO 2 during the 4 hour measurement period of the first week, suggesting that pressure fluctuations affect CO 2 transport but not production. Compared to ST, NT and ND show significantly higher CO 2 -C fluxes at lower pressure than ambient pressure for 4 and 12 hour mea surement periods during the first week and for a 12 hour measurement period during the second week. The NT and ND treatments, which had higher bulk densities, lower air-filled pore spaces and greater tortuosity of pore space than the ST treatment, show an effect of pressure change (low pressure vs. ambient). This suggests that occasional change in wind induced pressure fluctuation can affect CO 2 -C fluxes differently from soils that vary in soil bulk density and porosity. The results of the present study suggest that atmospheric pressure fluctuations on soils may influence CO 2 fluxes from soils and the affect w ill vary with change in the soil bulk density and porosity. Changes in atmosphe ric pressure however, were found to have short term differential effects on CO 2 fluxes from simulated tilled soils. Therefore it was felt unnecessary to simulate the effects of occasional low pressure caused by the wind speed at the soil surface. Moreover, the focus of this project was to quantify the reductions in CO 2 emissions with Cross-slot No-tillage cultiv ation compared with full cultivation and develop either a soil test or decomposition model for quantitatively determining reductions in CO 2 losses with No-tillage. All further studies we re conducted at ambient pressure conditions. 98 3.7 References Akbolat, D., Evrendilek, F., Coskan, A., & Ekinci, K. (2009). Quantifying soil respiration in response to short-term tillage practices: a case study in southern Turkey. Acta Agriculturae Scandinavica Section B-Soil and Plant Science, 59 (1 ), 50-56. Alavoine, G., Houlbert, J. C., & Nicolardot , B. (2008). Comparison of three methods to determine C decomposition of organic materials in soils under controlled conditions. Pedobiologia, 52 (1), 61- 68. Alvarez, R., Alvarez, C. R., & Lorenzo, G. (2001 ). Carbon dioxide fluxes following tillage from a mollisol in the Argentine Rolling Pampa. 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Comparison of field methods for measuring soil respiration: a static alkali absorpti on method and two dynamic closed chamber methods. Forest Ecology and Management, 170 (1 -3), 189-197. 104 Chapter-4 Carbon dioxide emissions from soils following rotary ? and No-tillage seed bed preparation under field and controlled laboratory conditions 4.1 Introduction No-tillage practice (NT) is globally recogni sed by scientists as a possible alternative to conventional tillage practice (CT) because it is highly be neficial in soil and water conservation and reduces production costs (Melero et al. 2011; Regina and Alakukku 2010; Moussa-Machraoui et al. 2010; Lal 2009; Christopher et al. 2009). Long-term No-tillage practice restores the soil organic carbon (SOC) lost as carbon dioxide due to conventional tillage practices thereby offsetting emissions by fossil fuel and alleviating concerns about projected global climate change (Blanco-Canqui and Lal 2008; Conant et al. 2007; West and Post 2002). A vast international literature clearly demonstrates that CO 2 emissions after sowing are significantly less from No-tilled than convent ionally tilled soils (Reicosky and Lindstrom 1993; Reicosky et al. 1997; Dao 1998; Rochette and Angers 1999; Alvarez et al. 2001; Al- Kaisi and Yin 2005; Bauer et al. 2006; Alvaro-Fuentes et al. 2008; Ussiri and Lal 2009; Morell et al. 2 0 1 0 ). However, quantitative information on potential savings in soil CO 2 emissions with No-tillage compared to convent ional tillage is limited in New Zealand. A single study conducted by Aslam et al. (2000) found no differences in CO 2 emissions from NT and CT soils throughout the crop growing period for summer maize and winter oats. The degree of soil disturbance by the ?Baker boot? , a slot opener in the No-tillage drill used in that study, raises questions about the interpretation of the results. The ?Baker boot? slot opener underwent further development to become the current Cross Slot ? opener. In the latest No-tillage drills, the ?Baker boot? has been replaced with an advanced Cross Slot ? opener which causes negligible soil disturbance in comparison to the ?Baker boot? (Baker and Saxton 2007). This necessitated the need to conduct a study to provide better scientific information on potential savings in soil CO 2 emissions with No-tillage under New Zealand conditions. Therefore this study was planned with the following objectives: 1) to quantify CO 2 emissions from simulated tillage (ST) and NT on a ra nge of soils varying in physico-chemical 105 properties under laboratory conditions and 2) to verify under field conditions the reliability of differences in CO 2 fluxes observed between ST and NT treatments from laboratory studies. 4.2 Material and methods 4.2.1 Laboratory study 4.2.1.1 Sites and soils The five sites are located in the Manawatu region of North Island, New Zealand. These sites under annual crop rotation of pasture and cereals were established for Cross Slot ? No-tillage cultivation over the last 2 to 15 years to reduce CO 2 losses from those that occur during conventional tillage practices. All five sites were on the farms using Cross Slot ? No- tillage practice for seeding pasture and cereals. The location, classification and physico- chemical characteristics of the soils are given in Table 4.1. Table 4.1: Physico-chemical characteristics of the five soil sites (0-10cm). Clay mineralogy* Claridge (1961); Saggar et al. ( 1996; 1999) and Hedley et al. (2000) Glen Oroua Tangimoana Kiwitea Feilding Sanson field site location 40 0 18?58.33?S 175 0 21?46.40?E 40 0 17?33.25?S 175 0 17?16.95?E 40 0 3?56.79?S 175 0 43?10.87?E 40 0 15?50.88?S 175 0 37?23.82?E 40?1 4 '3 4.33" S 175 ? 2 1 '2 7.07" E pH (1:2.5) 5.8 5.7 6.1 6.0 6.0 CEC (cmol c kg -1 ) 19.4 19.0 35.0 21.3 19.8 Olsen-P (mg kg -1 ) 33.9 45.8 14.7 14.2 21.5 SO 4 - S (mg kg -1 ) 11.0 10.8 31.5 14.0 8.3 Total C ( g kg -1 ) 43.5 43.3 85.0 30.3 39.7 Total N ( g kg -1 ) 3.7 3.7 8.7 2.8 3.5 Bulk Density (Mg m -3 ) 1.02 0.98 0.76 1.18 1.07 Soil type Carnarvon black sandy loam Carnarvon black sandy loam Dannevirke silt loam Ohakea stony silt loam Ohakea silt loam Soil Classification USDA Typic Humaquept Typic Humaquept Typic Hapludand Typic Epiaqualf Typic Epiaqualf NZ Concretionary Sandy Gley soil Concretionary Sandy Gley soil Typic Orthic Allophanic soil Typic Orthic Gley soil Typic Orthic Gley soil Sand (%) 71 57 15 21 17 Silt (%) 18 31 55 51 59 Clay (%) 11 12 30 28 24 No-tillage duration (years) 2 15 7 8 11 Clay mineralogy* Micaceous Micaceous Allophanic Smectitic Smectitic 106 4.2.1.2 Collection of soil cores and simulation of tillage Soil cores from all the sites were sampled for CO 2 measurements during December 2009 and January 2010 following the cultivation of pasture in late spring/early summer. All the four soil sites were sprayed with glyphosate herbicide prior to seeding. Two tillage treatments: simulated-tillage (ST) and No-tilla ge (NT) were compared; the third treatment was a non-disturbed (ND ) control. The control treatm ent had no soil disturbance by tillage. Thirty six in-situ soil cores (10 cm diameter, 10 cm depth) were collected at a soil depth of 0-10 cm from each site. Twenty four ND cores were collected before the start of field tillage operations from 4 different lo cations on each farm, six from each location. Twelve NT treatment cores were collected ov er the slots immediately after the Cross Slot ? No-tillage drilling, three cores from each loca tion. Each treatment comprised four replicates and each replicate was composed of three intact cores collected from one sampling location. Twelve out of twenty four ND cores were br oken up in the laboratory to simulate tillage treatment. The ST treatment was formulated from three in-situ soil cores by emptying the soil, breaking it into pieces, thoroughly mixing and packing it in a plastic container (19 cm diameter; 10 cm high). Carbon dioxide measurements for all the thre e treatments started after 2 hours of mixing the soils for the ST treatment. Moist ure contents in the cores and container as sampled from the field were maintained throughout the experiment by weighing the soil cores and spraying the required amount of deio nised water onto the surface. Measurements continued for 92 days (Glen Oroua), 83 days (T angimoana), 81 days (Kiwitea), and 54 days (Feilding) until the emissions subsided and th e differences between the treatments became negligible. 4.2.1.3 Carbon dioxide measurements Carbon dioxide flux production was measured using the alkali trap method. Soil in- situ cores and plastic containers filled with soil were placed in closed base static chambers and CO 2 measurements were taken on a daily basis at constant temperature (23 0 C) by placing a plastic petri dish containing 30 ml of 1M NaOH within each chamber. Chambers were sealed with air tight lids to control the leakage of carbon dioxide. After a 4 hour absorption period, the plastic petri dishes containing Na OH were removed and the total amount of CO 2 absorbed in each solution was determined by titrating NaOH with dilute 0.2M HCl. All the chemicals used were of analytical grade and the solutions were prepared using deionised water. The solution of 0.2M HCl was standardised against Na 2 CO 3 as per the method 107 outlined by Lambert et al. (1949). A working solution of 1.0M NaOH was prepared and the molarity was checked against standardised acid. 4.2.1.4 Calculation of CO2 flux for NT cores Carbon dioxide fluxes measured from the ND cores were used to calculate CO 2 fluxes from the actual area disturbed by the Cross Slot ? opener in NT treatment cores. To calculate CO 2 flux from ST, NT and ND treatments the amount of CO 2 emitted from each chamber was divided by the surface areas of 3 in-situ cores. Further, CO 2 flux from the actual area disturbed by the Cross Slot ? opener in NT treatment cores was calculated by determining the area of the portion disturbed by the Cross Slot ? opener and then by subtracting that area from the total surface area of the core, giving the undisturbed surface area. Total surface area of the core= 78.57 cm 2 Area of the core disturbed by the Cross Slot ? opener=43.44 cm 2 (Calculated from the Cross Slot ? width=4.5cm) Area of the core left undisturbed by Cross Slot ? opener=35.13 cm 2 Figure.4.1: Portion of the soil core disturbed by the Cross Slot ? opener shown by the width of the Cross Slot ? . Ratio of the core area disturbed and ar ea left undisturbed by the Cross Slot ? opener was determined by dividing the area disturbed by the Cross Slot ? opener by the total surface area of the core (43.44/78.57= 0.55). Similarly, the ratio of the area left undisturbed was calculated (35.13/78.57=0.45). 4.5 cm Cross Slot? Width ND ND Core diameter=10cm 4.5cm 108 The actual CO 2 flux contributed by the disturbed porti on of the NT treatment core was then calculated from the measured CO 2 fluxes from the NT and ND treatments as: NT C flux= Calculated NT C flux x 0.55+ ND C flux x 0.45 Calculated NT C flux = ((NT C flux - (ND C flux x 0.45))/0.55) ????..???..? (Eq. 4.1) where NT C flux = CO 2 flux from the NT treatment core ND C flux = CO 2 flux from ND treatment core The field CO 2 fluxes from the NT treatment were estimated by taking into account the proportions of disturbed and undisturbed field areas by the Cross Slot ? No-tillage drill system. Drill width=450 cm Number of Cross Slot ? openers in the drill=29 Portion disturbed by Cross Slot ? openers= Number of Cross Slots ? ? Width of Cross Slot ? 29 x 4.5=130.5 cm Ratios of the portion disturbe d and undisturbed by Cross Slot ? openers with one pass of drill: NT= (130.5/450) =0.29; ND= (1-0.29 ) =0.71 NT (0.29): ND (0.71 ). Field NT CO 2 flux= Calculated NT C flux?0.29+NDc flux? 0.71 ??????.? (Eq. 4.2) 4.2.1.5 Soil moisture content (SMC) The gravimetric SMC was measured at 0-10 cm depth. Replicated soil samples were collected from respective sites on the same date when soil cores were collected for carbon dioxide measurements. Field moist samples were weighed, oven-dried (105 oC) to constant mass, and weighed again. The final mass ( Ms ), and the difference between the field moist and dry masses ( Mw ), were used to calculate the gravimetric SMC = ( Mw/Ms ) X 100. Gravimetric SMC (%) for the four soil si tes were: Glen Oroua (46.1%); Tangimoana (41.9%); Kiwitea (60.6% ); Feilding (30.8%) at the time of sampling the soil cores. 4.2.1.6 Cumulative CO2 emissions Cumulative CO 2 -C emissions for the whole measurement period were calculated by linear interpolations of the measured fluxes and integrated over time for all the soil sites. 109 4.2.2 Field study Following CO 2 measurements from the intact soil cores collected from four different soil sites during the summer season (December 2009 and January 2010 ) , CO 2 measurements were made in the field during the autumn (April to July 2010) and summer (November 2010 to February 2011) seasons. 4.2.2.1 Site and soil The field experiment was carried out at a site near Sanson planted in barley ( Hordeum vulgare L.) during summer and ryegrass ( Lolium multiflorum L.) pasture during the autumn season. The site was sprayed with glyphosate herbicide prior to seeding. The location, classification and physico-chemical char acteristics of the soil are given in Table 4.1. 4.2.2.2 Establishment of field experiment The three tillage treatments compared during the field trial were: 1) tillage with a rotary tiller (RT) to a depth of 10 cm followed by bar harrow, 2) direct seeding with a Cross Slot ? No-tillage drill (NT), and 3) a non-disturb ed (ND) control. The control treatment had no soil disturbance by tillage. A portion (length 90m; width 18m) of a 1 hectare paddock was selected to conduct the trial. To compare three treatments this ex perimental area was equally divided into three segments of 90m x 6m each. For gas measurements, PVC chambers (diameter 23.5 cm; height 21 cm) were randomly installed in each segment, immediately after the completion of tillage operations. Each chamber had an inte rnal half-turn locking system and a greased O- ring which forms a gas-tight seal when closed with a lid (Saggar et al. 200 4). Each chamber was inserted 5 cm into the soil to avoid any leakage of CO 2 out of the chamber. During the autumn (April to July 2010) seeding of pasture, the number of PVC chambers differed between the three treatments. A number of twelve, four and eight replicate chambers were installed in RT, NT and ND treatments, respectively. Chambers were installed over the slots after Cross Slot ? No-tillage seeding. Carbon dioxide measurements were made for 110 days during the autumn season. To measure the CO 2 flux due to disturbance from rotary tilled soils, NaOH trap solutions were placed immediately after tillage operations (i.e. 0-4 hours) in four of the twelve chambers. In the remaining eight chambers NaOH trap solutions were placed two hours after the completion of tillage operations (i.e. 2-6 hours). In the NT and ND treatments, NaOH trap solutions were placed 110 two hours after the completion of tillage operations. No changes were made to the existing field set-up during the summer season. During the summer seeding of barley th e same three (RT, NT and ND) tillage treatments were compared and the number of PV C chambers did not differ between the three treatments. Eight replicate ch ambers were installed in each RT, NT and ND treatment to measure CO 2 losses and measurements were taken intermittently for 99 days. The NaOH trap solutions were placed two hours after the completion of tillage operations in all three treatments. The soil surface in and 1.0m around the chambers was always kept free of vegetation to avoid the influence of plants on CO 2 measurements as the objective was to quantify the short term C loss due to the deco mposition of crop or pastures residues after tillage. Although the plant-free surface doe s not simulate standard agronomic practice, it does avoid the complication of plant and root respiration being confused with microbial respiration during the decomposition of residues. Measurements were normally taken between 9 am and 1pm. A two hour period is required after the cham ber installation for all the greenhouse gas fluxes to come to equilibrium (Field prot ocol, Personal Communication Surinder Saggar). A parallel simulated tillage trial (i.e. taking CO 2 measurements from soil in-situ cores similar to the study conducted under laboratory conditions in December 2009 and January 2010 as in section 4.2.1) was conducted along with the field CO 2 measurements. During the autumn season, closed base chambers enclosin g the soil cores were placed in the field. During the summer season chambers we re placed in the laboratory at 23 oC to compare the CO 2 emissions under controlled conditions with the field emissions. Moisture content in the soil cores was maintained at original levels at which they were sampled from the field i.e.15.8 per cent throughout the measurement period. 4.2.2.3 Carbon dioxide measurements Carbon dioxide was measured using an alkali trap method similar to that used in the laboratory trial described in Section 4.2.1.3. 4.2.2.4 Soil temperature and moisture content Soil temperature was measured with iButton data loggers (DS1921 Z) which can measure a temperature range -5 oC to +26 oC with ?1 oC accuracy. Temperature buttons were buried at 10 cm depth in the soil adjacent to the chambers for all three treatments. Buttons were programmed to measure soil temperature bi-hourly. Gravimetric SMC was measured at 0-10 cm depth on the replicated soil samples collected from the three tillage treatments on the day of CO 2 measurement as described in section 4.2.1.5. 111 4.2.2.5 Cumulative CO2 emissions Cumulative CO 2 -C emissions for each season were calculated by linear interpolations of the measured fluxes and integrated over time. 4.2.3 Collection of soil samples for physical and chemical analysis Soils samples for physical and chemical anal ysis were collected in triplicate (0 -10 cm soil depth) from all five sites on the sa me day as soil cores were collected for CO 2 measurements. Each replicate was composed of 12 cores (2.5 cm diameter) randomly collected across the field. Soil samples were air dried and ground to pass through a 2mm sieve for further analysis. 4.2.3.1 Physical analysis Particle size distribution was determine d by the pipette method of Claydon (1989). For soil bulk density determination, undisturbe d soil cores (10 cm diameter, 10 cm depth) were taken and soil bulk density was calculated by dividing the mass of oven dried soil by the volume of the soil core. 4.2.3.2 Chemical analysis Soil pH (1:2.5) and cation exchange cap acity (CEC) was analysed according to Blackmore et al. (1987). Olsen P and SO 4 -S were determined according to Olsen et al. (1954) and Blackmore et al. (1987). A portion of each sample was ground to pass through a 0.2 mm sieve for total carbon and nitrogen analysis using a LECO induction furnace (Blackmore et al. 1987). 4.2.4 Collection of climate data Weather data was obtained from the National Climate Database interface ( http://cliflo.niwa.co.nz/ ). For the study period, April 2010 to July 2010 and November 2010 to February 2011, daily air temperature and precipitation data was collected. 112 Figure.4.2: Air temperature and precipitation at the Sanson field site from April 2010 to February 2011. 4.2.5 Measurements of above- and below-ground residue inputs Pasture residues were collected from all five soil sites. At each site three replicate plots measuring 1m x 1m were marked. Pasture grass was sprayed with glyphosate and after 10 days five soils cores (10 cm diameter) were taken to a depth of 10 cm from all the three replicates. To simulate the grazed conditions above-ground residues were cut to 2cm height. Roots and above ground parts of the pasture grass were then separated by gentle shaking and wet sieving. Oven dried (65 0 C) samples of above ground pa sture and root biomass from each core were used to calculate the residue yield. 4.2.6 Statistical analysis An analysis of variance using SAS software (9.1) was performed on CO 2 fluxes from the three tillage treatments using the General Linear Model (GLM) procedure. Mean comparisons between CO 2 fluxes were done using Fisher?s least significant difference (LSD ) at 5% level of significance. Pearson correlati on and regression coefficients were determined using SAS software (9.1) to determine the relationship between the total amount of CO 2 lost till day-54 under laboratory conditions and differ ent soil properties. All the soil replicates were used in correlation and regression analysis 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 - 1 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 7-Apr 24-Apr 11-May 28-May 14-Jun 1-Jul 18-Jul 4-Aug 21-Aug 7-Sep 24-Sep 11-Oct 28-Oct 14-Nov 1-Dec 18-Dec 4-Jan 21-Jan 7-Feb 24-Feb Precipitation (mm) Temperature ( o C) Date Pr ecipitat ion Maximum Air Temperat ure Minim um Air Temper atur e Sa mpli ng Dates 113 4.3 Results 4.3.1 Laboratory study In the laboratory study, the trends of the CO 2 -C fluxes were similar in both tillage treatments and non-disturbed control in all the soil sites, with high peaks of CO 2 -C observed for the first few days and low rather constant CO 2 -C fluxes afterwards. The daily CO 2 -C fluxes (kg CO 2 -C ha -1 d-1 ) for the ST, NT and ND treatments from all the four soil sites are shown in Figures 4.3 a, b, c, d. During the 92 days measurement period for the Glen Oroua site (Figure.4.3.a), significantly higher CO 2 -C fluxes from the ST than the NT and ND treatments were observed for the first 10 da ys; thereafter the differences in the three treatments narrowed, with CO 2 -C fluxes from NT and ND gr adually becoming larger than ST. Significantly higher CO 2 -C fluxes from NT than ND were observed only on day-1 of the measurement period. From day-33 of the measurement period CO 2 -C fluxes from the NT and ND treatments were either similar or higher th an the ST treatment. For the Tangimoana site, during the 83 days of the measurement significantly higher CO 2 -C fluxes from the ST than the NT and ND treatments were observed for th e first 24 days and thereafter the differences in CO 2 -C fluxes narrowed, with CO 2 -C fluxes from ND gradually becoming larger than NT and ST from day-41 (Figure.4.3.b). No signif icant differences were observed between CO 2 -C fluxes from NT and ND throughout the measurement period. During the 81 days of measurements for the Kiwitea site, the ST tr eatment emitted significantly higher CO 2 -C than the NT treatment, but only for the first 6 days. From day-20 onwards CO 2 -C fluxes from the ND treatment become larger than from the NT and ST treatments (Figure.4.3.c). For the Feilding site (Figure.4.3.d), the CO 2 -C fluxes were significantly higher from ST than NT and ND for only the first 5 days of the 54 day measurement period. From day-37 of the measurement period, fluxes from ND were eith er similar or higher than from the ST treatment. Comparing all the soil sites, the Kiw itea soil site produced higher initial CO 2 -C fluxes and the Feilding site produced lowest initial CO 2 -C fluxes from all the treatments in comparison to the other soil sites. The accumulated CO 2 -C emissions from the Glen Oroua, Tangimoana, Kiwitea and Feilding soil sites from ST were 2303, 3264, 29 05, 1086 kg CO 2 - C ha-1 ; NT were 2349, 2871, 2789, 973 kg CO 2 -C ha -1 and ND were 249 0, 3023, 3008, 953 kg CO 2 -C ha -1 (Figure. 4.4). None of the soils from the four sites showed any significant difference in the accumulated CO 2 -C emissions between the ST, NT and ND treatments. The 114 amounts of residue input were highest at Tangimoana (8.3 Mg ha -1 ) followed by Kiwitea (8.0 Mg ha -1 ), Glen Oroua (7.5 Mg ha -1 ) and Feilding (3.1 Mg ha -1 ) sites. Amongst sites, the amount of CO 2 -C lost was maximum from the Tangimoana site and minimum from the Feilding site. One ca n argue that it might be due to a shorter measurement period of 54 days but when the total CO 2 -C lost from all the soils till day-54 were compared, even then the CO 2 -C loss was lowest from the Feilding site (Table 4.2). Table 4.2: Cumulative average CO 2 -C emissions from simulated tillage (ST), No-tillage (NT) and Non-Disturbed (ND) treatments from Glen Oroua, Tangimoana, Kiwitea and Feilding site soils until day-54. Sites Cumulated CO 2 -C emissions (kg CO 2 -C ha -1 ) ST NT ND Glen Oroua 1822 1730 1821 Tangimoana 2708 2330 2452 Kiwitea 2468 2318 2521 Feilding 1086 973 953 Three soil types were used in the laboratory study; they varied in texture, total carbon content, gravimetric soil moisture content (GMC) and the amount of plant and root residues incorporated. A correlation analysis was conducted to relate these factors to the total amount of CO 2 -C lost till day-54 from the ST treatment for all the four soil sites. As the amount of residue incorporated and moisture contents were similar in all three tillage treatments in each soil, only the ST treatment was considered for the correlation and regression analysis. For the correlation analysis soil types were converted into indicator variables. Correlation analysis showed that except for soil type, all other factors were significantly correlated with the total amount of CO 2 -C lost until day-54 from the ST treatment (Table 4.3). Therefore soil type was not included in multiple regression analysis which was conducted to find out which of these three factors i.e. total carbon content, gravimetric soil moisture content (GMC) and amount of plant and root residue incorporated have a significant effect on CO 2 -C emissions. The multiple re gression (Table 4.4) showed that out of the three factors, residues incorporated had a significant effect on the CO 2 -C lost from the ST treatment. 115 Table 4.3: Pearson correlation coefficients (R) of the total amount of CO 2 -C lost until day 54 during laboratory incubation from the ST treatment and different soil factors of the four test soils. Soil factors R Gravimetric moisture content (%) 0.57* Total carbon content (g kg -1 ) 0.57* Residues incorporated (Mg ha -1 ) 0.83** Soil type 0.38 R: Correlation coefficient *Correlation coefficient significant at p < 0.05 **Correlation coefficient significant at p < 0.001 Table 4.4: Multiple linear regression model between soil CO 2 -C emissions till day 54 during laboratory incubation and gravimetric moisture content (GMC), total carbon content (TC) and residues incorporated (RES) for the ST treatment on the four test soils. R 2 : Coefficient of determination **Coefficient of determination significant at p < 0.001 Tillage treatment Multiple linear regression R 2 ST 668 - 18.6GMC + 12.3TC + 232RES** 0.72 116 (a) (b) (c) (d) Figure.4.3: Daily average CO 2 -C fluxes as influenced by tillage (ST, Simulated tillage; NT, No-tillage and ND, Non-Disturbed) from (a) Glen Oroua, (b) Tangimoana, (c) Kiwitea and (d) Feilding site soils. Each value represents the mean of four replicates and vertical bars above represent LSD (P < 0.05) for comparison among tillage treatments where significant differences were found. 0 25 50 75 10 0 12 5 15 0 17 5 0 12 24 36 48 60 72 84 96 kg CO 2-C ha -1 d -1 Days after til l ag e S T N T N D 0 25 50 75 10 0 12 5 15 0 17 5 0 12 24 36 48 60 72 84 96 kg CO 2-C ha -1 d -1 Days after til l ag e S T N T N D 0 25 50 75 10 0 12 5 15 0 17 5 0 12 24 36 48 60 72 84 96 kg CO 2-C ha -1 d-1 Days after til l ag e S T N T N D 0 25 50 75 10 0 12 5 15 0 17 5 0 12 24 36 48 60 72 84 96 kg CO 2-Cha -1 d -1 Days after til l ag e ST NT ND 117 Figure.4.4: Cumulative average CO 2 -C emissions from simulated tillage (ST), No-tillage (NT) and Non-Disturbed (ND) treatments from Glen Oroua, Tangimoana, Kiwitea and Feilding site soils along with standard errors (?) shown by vertical bars. N.S. stands for not significant 4.3.2 Field study During the autumn season CO 2 -C fluxes were measured continuously for the first 27 days and intermittently on days 36, 43, 49, 76 and 110 (Figure.4.5.a). Daily CO 2 -C fluxes ranged from 17.5-44.0 kg CO 2 -C ha -1 d-1 for RT; 15.6-38.0 kg CO 2 -C ha -1 d-1 for NT and 15.1- 35.4 kg CO 2 -C ha -1 d-1 for the ND treatment. During the 110-day measurement period in the autumn season RT gave higher daily CO 2 -C fluxes than NT and ND treatments. Significantly higher CO 2 -C fluxes from RT than NT and ND tr eatments were observed in 25 out of 32 daily CO 2 -C measurement events. No significant differences were observed between CO 2 -C fluxes from NT and ND treatments. During the autumn season measurement period the cumulative amount of rainfall received was 288mm and soil temperature ranged between 4.7 and 16.2 oC. An early burst of CO 2 emission was measured during the autumn season only in the RT treatment. Carbon dioxide flux immed iately after tillage (i.e. 0-2 hours/ t-0 hours) (56.8 kg CO 2 -C ha -1 d-1 ) was significantly higher than flux measured after 2 hours (i.e. 2-6 hours/ t-2 hours) (41.5 kg CO 2 -C ha -1 d-1 ) (insert in Figure.4.5.a). The disturbance caused by tillage in the field significantly elevated the CO 2 fluxes for the first four hours only (0- 4 0 500 1000 1500 2000 2500 3000 3500 4000 Glen Oro ua Tangimoan a Kiwit ea Feilding kg CO 2-C ha -1 ST NT ND N. S . N. S . N. S. N.S. 118 hours). Daily rates estimated from the amount of CO 2 trapped during this period tends to overestimate daily CO 2 fluxes because the CO 2 trapped between 2-6 hours after tillage was considerably lower and more similar to the rates observed at 24 and 48 hours after tillage. The CO 2 flux rates from all twelve replicate chambers were used to estimate the average daily for the remainder of the experiment. During the summer season, CO 2 -C fluxes were measured on alternate days for the first 4 weeks and the frequency of subsequent measurements was reduced to 3, 4 and 7 days for the following 2 months (Figure.4.5.b). Daily CO 2 -C fluxes during the summer season ranged from 13.3-109.4 kg CO 2 -C ha -1 d-1 for RT; 10.9-93.2 kg CO 2 -C ha -1 d-1 for NT and 10.5-89.0 kg CO 2 -C ha -1 d-1 for ND treatment. Out of 30 CO 2 -C measurement events during the summer season, significantly higher CO 2 -C fluxes from the RT than the NT treatment were observed for 17 measurement events. The majority of these significant differences were observed during the first 35 days after tillage. Significant differences between CO 2 -C fluxes from NT and ND treatments were observe d only on day one. Carbon dioxide fluxes during the summer season showed variation depending on the GMC?s of the soil (Figure. 4.6). Gravimetric moisture content (GMC) ranged between 19.5% and 38.7% during the autumn season and varied from 9.5% to 39.8% during the summer season across all three RT, NT and ND treatments (Figures. 4.7 a, b). On average the GMC ?s during both seasons were in the order ND > NT > RT. Such an eff ect could be related to easier water percolation in rotary tilled soils than No-tillage and mo re compact soil conditions in the non-disturbed control treatment. Additionally, presence of th e surface crop residues in the NT and ND treatments decreases the evaporative loss of water from the NT and ND treatments compared to the RT treatment (Jarecki and Lal 2003). Average GMC was higher during the autumn season in comparison to the summer season. High GMC ?s in the summer season were observed only in the event of rainfall (Figures.4.2 & 4.7. b). Soil temperatures ranged between 4.7 and 16.2 oC during autumn and 16.5 and 23.6 oC during the summer season (Figures. 4.8 a, b). The soil temperatures in autumn were lower than those observed in summer. However, there were no differences in soil temperature among the tillage treatments during both seasons. The total amounts of CO 2 -C emitted from RT, NT and ND treatments during the autumn and summer seasons are shown in Figures. 4.10. a, b. Accumulated CO 2 -C emissions from RT, NT and ND treatments during the autumn season were 2580, 221 5 & 2405 kg CO 2 - C ha-1 and 3330, 2877 & 2807 kg CO 2 -C ha -1 during the summer season. The total amount of CO 2 -C emitted from RT was significantly higher than for NT and ND treatments during the 119 autumn and summer seasons. The amounts of CO 2 -C emitted from NT and ND treatments did not differ significantly during both the autumn and summer seasons. The residue inputs were higher in the summer (7.2 Mg ha -1 ) than in the autumn cultivation (5.3 Mg ha -1 ). The linear regression analysis relating CO 2 -C fluxes to soil temperature and moisture contents in respective tillage treatments during the summer and autumn seasons are given in Table 4.5. No relationship was observed between the CO 2 -C fluxes and soil temperature during both seasons. No relationship was observed between the CO 2 -C fluxes and soil moisture during the autumn season, but a we ak and significant relationship was observed during the summer season. Table 4.5: Linear regression coeff icients of determination (R 2 ) comparing the CO 2 -C fluxes from the RT, NT and ND treatments with grav imetric moisture content (GMC %) and soil temperature during the autumn and summer seasons. Soil factors Coefficient of determination Autumn season Coefficient of determination Summer season RT NT ND RT NT ND Soil temperature 0.02 0.01 0.01 0.003 0.003 0.004 GMC (%) 0.18 0.01 0.02 0.25* 0.29* 0.16* R 2 : Coefficient of determination *Coefficient of determination significant at p < 0.05 120 (a) (b) Figure.4.5: Daily average CO 2 -C fluxes as influenced by tillage (RT, Rotary tillage; NT, No-tillage and ND, Non-Disturbed) during (a) autumn and (b) summer season from the Sanson field site. Each value represents the mean of twelve replicates for ST, four replicates for NT, eight replicates for ND treatment for autumn season and eight replicates for all three treatments during the summer season with standard errors (?) shown by vertical bars. Vertical bars above represent LSD (P < 0.05) for comparison among tillage treatments where significant differences were found. I nsert in Figure.4.5.a shows the CO 2 -C flux immediately after tillage (t-0 hours) and after 2 hours (t-2 hours) of the tillage event. 0 20 40 60 80 100 120 140 0 10 20 30 40 50 60 70 80 90 100 110 kg CO 2-C ha -1 d -1 D a ys after tilla g e RT NT ND 121 Figure.4.6: Daily average CO 2 -C fluxes from the rotary and No-tilled soils during the summer season from the Sanson field site affected by soil GMC. 0 1 0 2 0 3 0 4 0 5 0 6 0 0 2 0 4 0 6 0 8 0 1 00 1 20 0 10 20 30 40 50 60 70 80 90 100 110 120 GMC (%) CO 2 flux (kg CO 2-C ha -1 d-1 ) D a ys after tilla g e R T flux N T flux R T GM C N T GM C 122 (a) (b) Figure.4.7: Gravimetric moisture content (GMC %) at 0-10 cm depth during 4 hour CO 2 measurements (a) autumn and (b) summer season. (a) (b) Figure.4.8: Soil temperature at 0-10 cm depth for 4 hour CO 2 measurement period during (a) autumn season and (b) summer season. 0 5 10 15 20 25 30 35 40 45 0 10 20 30 40 50 60 70 80 90 10 0 11 0 GMC (%) Days after til l ag e R T N T N D 0 5 10 15 20 25 30 35 40 45 0 10 20 30 40 50 60 70 80 90 10 0 11 0 GMC (%) Days after til l ag e R T N T N D 0 5 10 15 20 25 0 10 20 30 40 50 60 70 80 90 10 0 11 0 Soil temperature ( o C) Days after til l ag e R T N T N D 0 5 10 15 20 25 0 10 20 30 40 50 60 70 80 90 10 0 11 0 Soil Temperature ( o C) Days after til l ag e R T N T N D 123 In conjunction with field measurements, CO 2 -C measurements were repeated with in- situ soil cores placed under field conditions during the autumn season and under laboratory conditions during the summer season with simulated tillage treatments as applied in earlier laboratory studies during the summer season (D ecember 2009 and January 2010 as in section 4.2.1). Measurements continued for 110 days during the autumn season and 99 days for the summer season. The daily CO 2 -C fluxes from ST, NT and NT treatments during the autumn and summer seasons are shown in Figures 4.9.a, b. During the autumn season CO 2 -C fluxes ranged from 18.4-55.1 kg CO 2 -C ha -1 d-1 for ST treatment; 13.3-44.2 kg CO 2 -C ha -1 d-1 for NT treatment and 12.4-40.1 kg CO 2 -C ha -1 d-1 for ND treatment. The total amount of CO 2 -C lost during the autumn season was significantly higher in ST (2960 kg CO 2 -C ha -1 ) than NT (2149 kg CO 2 -C ha -1 ) and ND (2027 kg CO 2 -C ha -1 ) treatments (Figure.4.10.a). No significant differences between NT and ND treatments were observed. During the summer season CO 2 -C fluxes ranged from 11.5 -121.0 kg CO 2 -C ha -1 d-1 for ST treatment; 12.0-7 5.7 kg CO 2 -C ha -1 d-1 for NT treatment and 11.1- 52.8 kg CO 2 -C ha - 1 d-1 for ND treatment. The cumulated CO 2 -C emissions from ST (2253 kg CO 2 -C ha -1 ) were higher than NT (2023 kg CO 2 -C ha -1 ) and ND (1860 kg CO 2 -C ha -1 ) treatments during the summer season. However, no significant differ ences were observed between ST, NT and ND treatments (Figure.4.10.b). The CO 2 -C emissions measured from in-situ soil cores during the autumn season were similar to those reported from the field measurements (~2500 kg CO 2 -C ha -1 ); however, a similar trend was not observed during the summer season. Under field conditions CO 2 -C emissions (Figure 4.10.b) were ~3000 kg CO 2 -C ha -1 compared with ~ 2000 kg CO 2 -C ha -1 for laboratory simulated conditions during the summer season. 124 (a) (b) Figure.4.9: Daily average CO 2 -C fluxes as influenced by tillage (ST, Simulated tillage; NT, No-tillage and ND, Non-Disturbed) during (a) autumn under field conditions and (b) summer season under laboratory conditions. Each value re presents the mean of four replicates and vertical bars above represent LSD (P < 0.05) for comparison among tillage treatments where significant differences were found. (a) (b) Figure.4.10: Cumulative average CO 2 -C emissions as influenced by tillage (ST, Simulated tillage; NT, No-tillage and ND, Non-Disturbe d) during (a) autumn and (b) summer season with standard errors (?) shown by vertical ba rs. Letters indicate statistically significant differences (p < 0.05). N.S. stands for not significant. 0 20 40 60 80 10 0 12 0 14 0 16 0 0 10 20 30 40 50 60 70 80 90 10 0 11 0 kg CO 2-C ha -1 d -1 Days after til l ag e S T N T N D 0 20 40 60 80 10 0 12 0 14 0 16 0 0 10 20 30 40 50 60 70 80 90 10 0 11 0 kg CO 2-C ha -1 d -1 Days after til l ag e S T N T N D 0 500 1000 1500 2000 2500 3000 3500 Field measurement s I n-sit u so il cores measurement s kg CO 2-C ha -1 R T / S T N T N D a a b b b b 0 500 1000 1500 2000 2500 3000 3500 Field measurement s So il cores measur ement s kg CO 2-C ha -1 R T / S T N T N D b a b N.S. 125 4.4 Discussion 4.4.1 Laboratory study Carbon dioxide measurements from the soils collected from the four different sites were carried out at constant laboratory temperature (23 oC). Soil moisture contents of the respective soils were maintained at their original level at which they were sampled from the field. Significantly higher initial CO 2 -C fluxes from ST than NT and ND treatments could be attributed to physical release of the CO 2 entrapped in the soil pores followed by microbial decomposition of easily decomposable organic matter (Curtin et al. 2000; Elder and Lal 2008). Differences in the CO 2 -C fluxes between ST and NT treatments ceased when tillage-stimulated decomposition diminished , likely due to the exhaustion of easily decomposable residue substrates. Irresp ective of the soil sites higher CO 2 -C fluxes from ND than ST and NT treatments were observed af ter a few days of experiment initiation. This could be due to decrease in CO 2 concentrations in ST and NT soil pores due to rapid gas exchange or cessation of microbial decompositi on due to exhaustion of easily decomposable residue substrates. Reicosky et al. (2008) compared CO 2 concentrations in mould board ploughed and NT soils under field conditions and observed higher CO 2 concentrations in NT soils (about 3.3%) than mould board plough ed soils (about 1.4%) suggesting higher CO 2 concentrations in least disturbed soils. As the experiment was conducted under control led conditions, the differences in the total amount of CO 2 -C lost during the measurement peri ods for the four test soils were attributed to differences in soil texture, grav imetric moisture content, total carbon content, and the amount of plant and root residues incorporated, respectively. The total amounts of CO 2 -C lost during the entire measurement period s from different soils irrespective of the tillage treatments are affected by the amounts of residue incorporated. A multiple regression of cumulative CO 2 -C emissions over days-54 from the ST treatment for the four soils with gravimetric soil moisture content, total ca rbon content and amount of residues incorporated also confirmed that amount of residues incorporated was an important factor that affected the total amount of CO 2 -C lost from soils (Table. 4.4). The total amount of CO 2 -C lost from the three tillage treatments did not show any significant differences in all the four soils. Lack of variation in soil moisture and temperature conditions in the laboratory could explain why the differences in total amount of CO 2 -C lost from different ti llage treatments were not significant. 126 Although the differences between cumulative CO 2 -C emissions from ST, NT and ND treatments were not significant, the amount of CO 2 -C lost from the ND treatment was either similar or higher than the NT treatment in all four soil sites (Glen Oroua, Tangimoana, Kiwitea and Feilding) and higher even than th e ST treatment in the Glen Oroua and Kiwitea sites (Figure 4.4). Higher CO 2 -C fluxes from the ND than ST and NT treatments after (few days) experiment initiation were th e cause of the higher total CO 2 -C emissions from ND than NT and ST treatments. 4.4.2 Field study The early burst of CO 2 -C after rotary tillage (insert in Figure.4.5.a) measured during the autumn season was likely due to an increase in the air transport coefficient resulting from soil loosening. Rochette and Angers (1999 ) measured significantly higher CO 2 fluxes immediately after mould board ploughing than in undisturbed soils and used the term degassing to explain the increase in CO 2 fluxes immediately following tillage resulting from changes in the physical characteristics of tilled soils. Degassing means the loss of CO 2 from loosened soils due to change in bulk densit y and air filled porosity. Reicosky & Lindstrom (1993); Reicosky et al. (1997) and Kessavalou et al. (1998) concluded that a sharp increase in CO 2 flux immediately after tillage was due to the physical release of the CO 2 entrapped and accumulated in soil pores from previous microbial activity. Comparing different tillage treatments, Al-Kaisi and Yin (2005) obs erved a decrease of 52 to 68% in CO 2 fluxes within the first two hours after tillage operations which was greater than the reductions of 26.9% observed within 2 hours of the tillage operation in this study. Significantly higher CO 2 emissions were observed from rotary tilled soils than the Cross Slot No-tillage seeded soils during th e summer and autumn seasons. These results were in accordance with the previous studies conducted on comparison of contrasting tillage systems on CO 2 emissions from a range of cropping soils (Table 2.2 Chapter-2). The average difference between the CO 2 -C emissions from the RT and NT treatments during autumn and summer were 3.4 and 4.7 kg CO 2 -C ha -1 d-1 (calculated by dividing the cumulated CO 2 -C emissions during the summer and autumn seasons by the number of days for which measurements were made), which are similar to the differences reported by the following studies reported in Table 2.2 Chapter-2; Dao ( 199 8); Ellert and Janzen (1999); Al-Kaisi and Yin (2005); Morell et al. (2010). Significantly higher daily CO 2 -C fluxes from RT than NT and ND treatments during the summer and autu mn seasons (Figures 4.5.a & b) could be attributed to accelerated soil organic matter decomposition due to tillage management. 127 Tillage incorporates the crop residues into the soil profile, breaks soil aggregates and modifies the soil environment i.e. soil temperature, moisture and aeration, resulting in enhanced soil organic matter decomposition (Alvarez et al. 2001; Six et al. 2002; Bauer et al. 2006). The CO 2 fluxes measured during this study period were similar to those reported by Aslam et al. (2000) under contrasting tillage sys tems in New Zealand conditions also using the absorption technique. Daily CO 2 -C fluxes during the summer season we re limited by soil moisture status. Irrespective of the tillage treatments, higher flux rates were measured on day-3 compared to the fluxes measured on day-1 because of an increase in soil moisture content (Figure 4.6) following rainfall on day-2 of the measuremen t period (Figure 4.2). Similar peaks in CO 2 -C fluxes were observed on day-30 and day-68 of the measurement period with increase in soil moisture content due to rainfall events. Bauer et al. (2006); Alvaro-Fuentes et al. (2008); Akbolat et al. (2009) and Morell et al. (2010) reported similar increases in CO 2 -C fluxes after rainfall events. Akinremi et al. (1999) suggested that higher CO 2 -C fluxes after a rainfall event was due to displacement of CO 2 from soil pores to the atmosphere due to water filling the soil pores, followed by an increase in microbial activity. In this study, rates of CO 2 -C fluxes remained high as long as the soil remained moist due to rainfall events, and fell as soil dried. The linear regression relating daily CO 2 -C fluxes from RT, NT and ND treatments with soil temperature and moisture content during the autumn and summer seasons showed that only soil moisture had a significant effect on daily CO 2 -C fluxes (Table 4.5) but only during the summer season. However, other rese archers concluded soil temperature was a major factor influencing soil CO 2 -C fluxes (Buyanovsky et al. 1986; Franzluebbers et al. 1995; Frank et al. 2002; Al-Kaisi and Yin 2005). After re viewing the interactive effects of soil physical factors and biological processes on soil atmosphere gas exchange, Smith et al. (2003) concluded that CO 2 production by aerobic respiration is temperature driven but becomes moisture dependent as soil dries. This could explain why a significant relationship was observed between daily CO 2 -C fluxes and soil moisture during the summer season. During the autumn season, the CO 2 -C flux measurements were made during April to July, soil temperatures were relatively low i.e. varied between 4.7 and 16.2 oC and soil moisture was generally stable over that period. This could explain why no significant effect was found between CO 2 -C fluxes and soil temperature and moisture. 128 However, the complex process of CO 2 production and release from soils could not be explained without taking into account the declin e of crop residues with time. This complex relationship is explained in Chapter-6. For the entire study period, irrespective of the tillage treatments, higher peaks of CO 2 -C fluxes were observed during the summer season in comparison to the autumn season. Bauer et al. (2006) observed a two fold increase in CO 2 fluxes from conventional and conservative tilled soils during summer compared to spring and autumn seasons. Results of the present study are consistent with the previous seasonal studies (Omonode et al. 2007; Akbolat et al. 2009; Iqbal et al. 2008; Ussiri and Lal 2009). Generally, air and soil temperature controlled the seasonal variations in CO 2 fluxes across all tillage treatments with high fluxes during summer when temperatures were high resulting in high biological activity, and low fluxes during autumn/winter when temperatures are low resulting in low biological activity. 4.4.3 Comparisons of CO2 emissions during laboratory and field studies The magnitude of CO 2 -C emissions from RT/ST, NT a nd ND treatments both in field and in-situ soil cores were similar (~2.5 Mg CO 2 -C ha -1 ) (Figure 4.10.a) during the autumn season, but during the summer season the magnitude of CO 2 -C emissions from RT/ST, NT and ND treatments both in the field and under laboratory conditions were not similar. Under field conditions CO 2 -C emissions (Figure 4.10.b) were ~3.0 Mg CO 2 -C ha -1 compared with ~ 2.0 Mg CO 2 -C ha -1 for laboratory simulated conditions. During the autumn season in-situ soil cores were placed in the field and were exposed to similar variable moisture and temperature conditions that might have resulted in similar magnitudes of CO 2 -C emission. During the summer season in-situ soil cores were placed under laboratory conditions. The lower CO 2 -C emissions measured under laboratory conditions than by field chambers may have been due to: a) lower soil moisture content (15.8% GMC) at the time of collection of the soil cores used in the laboratory compared with variable moistures in the field or b) contribution of CO 2 from the deeper soil layers (> 10 cm) in field chambers may have resulted in higher emissions in the field chambers. 4.5 Summary and conclusions Carbon dioxide (CO 2 ) emissions were measured for up to 3 months in the laboratory from four soils varying in physico-chemical p roperties and one soil in the field (autumn and summer seasons) using the alkali trap method following Cross Slot ? No-tillage cultivation, simulated conventional tillage (ST) and non-dis turbed (ND) control. The total amount of 129 CO 2 emitted from the four soils under laboratory conditions ranged between 1086 and 3264 kg CO 2 -C ha -1 for ST, 973 and 2871 kg CO 2 -C ha -1 for NT and 953 and 3023 kg CO 2 -C ha -1 for the ND treatment. In general, three out of four soils lost more CO 2 from the ST treatment (between 113 and 393 kg CO 2 -C ha -1 ) than they did for the NT treatment. Similarly, in the field the total CO 2 -C emissions were significantly higher from RT (2580 kg CO 2 -C ha -1 ) than NT (2215 kg CO 2 -C ha -1 ) treatment during the autumn season, and from RT(3330 kg CO 2 -C ha -1 ) compared with NT (2877 kg CO 2 -C ha -1 ) during the summer season. Results of the field study suggests that conversion of pasture to cropping and cropping to pasture resulted in a net annual conservation of 818 kg C ha -1 for NT soils. The average differences between the CO 2 -C emissions from the RT and NT treatments during autumn and summer were comparable to the differences reported by the previous studies comparing the CO 2 -C emissions from tilled and No-tilled soils. Carbon dioxide fluxes during the summer season showed variation depending upon the rainfall and GMC?s of the soil. Carbon dioxide measurements made from both the field chambers and in- situ soil cores following RT/ST or NT treatm ents were similar in magnitude (~2.5 Mg CO 2 - C ha-1 ) during the autumn season. During the autumn season in-situ soil cores were placed in the field and were exposed to similar vari able moisture and temperature conditions which resulted in the similar magnitude of CO 2 -C emissions from both field and in-situ soil cores. However, during the summer season the magnitude of CO 2 emissions from RT/ST, NT and ND treatments both in field and laborator y conditions were not similar. Under field conditions carbon dioxide emissions were ~3.0 Mg CO 2 -C ha -1 compared with ~ 2.0 Mg CO 2 -C ha -1 for laboratory simulated conditions during the summer season. During the summer season soil cores were placed under laboratory conditions and were maintained at constant temperature and moisture content. Lack of variation in soil moisture and temperature in laboratory incubated soil cores resulted in the difference in magnitude of CO 2 emissions between field and soil cores. Therefore the concept that soil cores from the field being brought into the laboratory and tillage-tr eatment simulated to give reliable estimates of CO 2 emissions under field conditions appears to be unworkable. The present study aimed to quantify CO 2 loss due to microbial decomposition of crop and pasture residues after tillage, which was the key reason why the soils under study in both the field and laboratory were left bare without root activity for the measurement periods. Overall, Cross Slot No-tillage seeding of barley and pasture resulted in a net annual conservation of 818 kg C ha -1 in soil which otherwise would have contributed 3.0 Mg CO 2 to the atmosphere. 130 4.6 References Akbolat, D., Evrendilek, F., Coskan, A., & Ekinci, K. (2009). Quantifying soil respiration in response to short-term tillage practices: a case study in southern Turkey. Acta Agriculturae Scandinavica Section B-Soil and Plant Science, 59 (1 ), 50-56. Akinremi, O. O., McGinn, S. M., & McLean, H. D. J. (1999). Effects of soil temperature and moisture on soil respiration in barley and fallow plots. Canadian Journal of Soil Science, 79 (1), 5-13. Al-Kaisi, M. M., & Yin, X. H. (2005). Tillage and crop residue effects on soil carbon and carbon dioxide emission in corn-soybean rotations. Journal of Environmental Quality, 34 (2), 437-445. Alvarez, R., Alvarez, C. R., & Lorenzo, G. (2001 ). Carbon dioxide fluxes following tillage from a mollisol in the Argentine Rolling Pampa. 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(2008). No-tillage and soil-profile carbon sequestration: An on-farm assessment. Soil Science Society of America Journal, 72(3), 693-7 01. Buyanovsky, G. A., Wagner, G. H., & Gantzer, C. J. (1986). Soil respiration in a winter- wheat ecosystem. Soil Science Society of America Journal, 50(2), 338-344. Christopher, S. F., Lal, R., & Mishra, U. (2009). Regional study of no-till effects on carbon sequestration in the Midwestern United States. Soil Science Society of America Journal, 73 (1), 207-216. 131 Claydon, J. J. (1989). Determination of particle size distribut ion of fine grained soils-Pipette method. : DS IR Division of Land and Soil Sciences Technical record LH5. Claridge, G. G. C. (196 1). Mineralogy and or igin of the yellow-brown sands and related soils. New Zealand Journal of Geology and Geophysics, 4 , 48-72. Conant, R. T., Easter, M., Paus tian, K., Swan, A., & Williams, S. (2007). Impacts of periodic tillage on soil C stocks: A synthesis. 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Comparative effects of conventional and no-tillage management on some soil properties under Mediterranean semi-ari d conditions in northwestern Tunisia. Soil & Tillage Research, 106 (2), 247-253. Olsen, S. R., Cole, C. V., Watanabe, F. S., & Dean, L. A. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA Department Circular , 939. Omonode, R. A., Vyn, T. J., Smith, D. R., Hegymegi, P., & Gal, A. (2007). Soil carbon dioxide and methane fluxes from long-term tillage systems in continuous corn and corn-soybean rotations. Soil & Tillage Research, 95 (1-2), 182-195. Regina, K., & Alakukku, L. (2010). Greenhouse gas fluxes in varying soils types under conventional and no-tillage practices. Soil & Tillage Research, 109 (2), 144 -152. Reicosky, D. C., & Lindstrom, M. J. (1993). Fall tillage method - effect on short-term carbon-dioxide flux from soil. Agronomy Journal, 85 (6), 1237-1243. Reicosky, D. C., Dugas, W. A., & Torbert, H. A. (1997). Tillage-induced soil carbon dioxide loss from different cropping systems. Soil & Tillage Research, 41 (1-2), 10 5-118. Reicosky, D. C., Gesch, R. W., Wagner, S. W., G ilbert, R. A., Wente, C. D., & Morris, D. R. (2008). Tillage and wind effects on soil CO 2 concentrations in muck soils. Soil & Tillage Research, 99 (2), 221-231. Rochette, P., & Angers, D. A. (1999). Soil surf ace carbon dioxide fluxes induced by spring, summer, and fall moldboard plowing in a sandy loam. Soil Science Society of America Journal, 63 ( 3 ) , 621-628. 133 Saggar, S., Andrew, R. M., Tate, K. R., Hedl ey, C. B., Rodda, N. J. , & Townsend, J. A. (2004). Modelling nitrous oxide emissions from dairy-grazed pastures. Nutrient Cycling in Agroecosystems, 68 (3), 243-255. Saggar, S., Parshotam, A., Sparling, G. P., Feltham, C. W., & Hart, P. B. S. (1996). C 14 labelled ryegrass turnover and residence times in soils varying in clay content and mineralogy. Soil Biology & Biochemistry, 28 (12), 1677-1686. Saggar, S., Parshotam, A., Hedley, C., & Salt, G. (1999 ). C 14 labelled glucose turnover in New Zealand soils. Soil Biology & Biochemistry, 31(14), 2025-2037. Six, J., Feller, C., Denef, K., Ogle, S. M., Sa , J. C. D., & Albrecht, A. (2002). Soil organic matter, biota and aggregation in temperate and tropical soils - Effects of no-tillage. Agronomie, 22 (7-8), 755 -775. Smith, K. A., Ball, T., Conen, F., Dobbie, K. E., Massheder, J., & Rey, A. (2003). Exchange of greenhouse gases between soil and atmosphere: interactions of soil physical factors and biological processes. European Journal of Soil Science, 54 (4), 779-791. Ussiri, D. A. N., & Lal, R. (2009). Long-ter m tillage effects on soil carbon storage and carbon dioxide emissions in continuous corn cropping system from an alfisol in Ohio. Soil & Tillage Research, 104 (1), 39-47. West, T. O., & Post, W. M. (2002). Soil orga nic carbon sequestration rates by tillage and crop rotation: A global data analysis. Soil Science Society of America Journal, 66 (6), 1930-1946. 134 Chapter-5 Influence of soil type and rates of residue addition on decomposition of residues in soils under controlled laboratory conditions and the relationship of CO2 emitted during the entire incubation period with labile soil C fractions 5.1 Introduction Soil organic carbon (SOC) is the traditional measure of soil organic matter (SOM) (Skjemstad et al. 2006), and an important indicator of soil quality due to its influence on soil physical, chemical and biological properties (Saggar et al. 2001; Chen et al. 2009) The SOC pool is comprised of labile and recalcitrant/passive C pools with varying turnover rates (Haynes 2005). The labile C pools, which turn over rapidly, respond quickly to land use change and soil management practices (Benbi et al. 2012; Toosi et al. 2012); and it has been suggested that they are the early indicators of SOC change (Xu et al. 2011). For this reason some C pools which respond quickly to agricultural management practices (e.g. microbial biomass- C, hot water extractable-C and oxidisable organic-C) may be able to be used to detect changes in SOC and therefore be used to predict CO2 losses from soils. In the earlier laboratory and field studies (Chapter-4), CO2 measurements were made from soils varying in texture (silt loam and sandy loam), moisture contents, and containing different amounts of residue. However it was difficult to ascertain the effect of texture and residue inputs on CO2 emissions due to the confounding effects of soil texture, moisture and amount of residue in these soils. Therefore, a laboratory incubation study was planned: 1) To evaluate the effect of soil type and residue input rates on residue decomposition; in order to establish a relationship between soil texture, residue input and CO2 losses, and 2) To identify the labile C pools contributing to these losses and establish a relationship between the oxidisable-C pool and CO2 losses. 135 5.2 Material and methods 5.2.1 Soils The four soils used were the Carnarvon black sandy loam (Carnarvon SL), Ohakea stony silt loam (Ohakea stony SiL), Ohakea silt loam (Ohakea SiL), and Horotiu silt loam (Horotiu SiL). Soil classes and relevant chemical properties are given in Table 5.1; chemical analysis was carried out by the procedures outlined in section 4.2.3.2 (Chapter-4). Three soils (Carnarvon SL, Ohakea stony SiL, Ohakea SiL) came from three different field sites in the Manawatu region and one soil (Horotiu SiL) came from the Waikato region in the North Island of New Zealand. Soils from the Manawatu region were used in the laboratory study (Chapter-4) for measuring CO2 fluxes. From each field site, four replicate surface soil (0-10 cm) samples were taken, and for each replicate 100 soil cores (2.5 cm diameter) were taken and pooled (sampling avoided recent dung and urine patches). Soil samples were collected during September 2010 i.e. early spring. The samples were sieved (< 2 mm) in the field moist state and stored at 4oC for about 10 to 15 days. These fresh, field-moist, sieved samples were used in the decomposition studies. Table 5.1: Chemical characteristics of the four soils (0-10cm) used in the incubation study. Clay mineralogy* Claridge (1961); Saggar et al. (1996; 1999) and Hedley et al. (2000) Carnarvon SL Ohakea stony SiL Ohakea SiL Horotiu SiL pH 5.7 6.0 6.0 5.8 CEC (cmolc kg-1) 19.0 21.3 19.8 35.0 Olsen-P (mg kg-1) 45.8 14.2 21.5 16.7 SO4-S (mg kg -1) 10.8 14.0 8.3 10.3 Total C (mg C g-1 soil) 43.8 28.8 38.6 50.3 Total N (mg C g-1 soil) 3.7 2.8 3.5 5.2 Soil type Carnarvon black sandy loam Ohakea stony silt loam Ohakea silt loam Horotiu silt loam Soil classification USDA Typic Humaquept Typic Epiaqualf Typic Epiaqualf Typic Hapludand NZ Concretionary Sandy Gley soil Typic Orthic Gley soil Typic Orthic Gley soil Typic Orthic Allophanic soil Particle size distribution Sand (%) 57 21 17 31 Silt (%) 31 51 59 42 Clay (%) 12 28 24 27 Field capacity moisture (%) 49.5 40.5 50.7 66.4 Clay mineralogy* Micaceous Smectitic Smectitic Amorphic 136 5.2.2 Plant material Ryegrass (Lolium multiflorum L.) pasture residues were collected from the three sites in the Manawatu region. At each site, three replicate plots measuring 1m x 1m were marked. Pasture grass was sprayed with glyphosate herbicide and after 10 days five soils cores (10 cm diameter) were taken to a depth of 10 cm from all three replicate plots. To simulate the grazed conditions above-ground, plant residues were cut to 2 cm in height. Roots and above- ground residues of the pasture grass were then separated by gentle sieving and stored at 4oC. Residues collected from all three sites were thoroughly mixed and chopped (< 2 cm in length) using a pair of scissors. Residues on dry weight basis contain 42.6%C, 1.35%N, 32.2% cellulose, 32.5% hemi cellulose, and 7.9 % lignin. Cellulose, hemicellulose and lignin contents were determined by the procedure outlined by Robertson and van Soest (1981). 5.2.3 Decomposition study The soils were adjusted to 80% of their field capacities. Four replicate soil samples equivalent to 25 g (oven dry) were mixed thoroughly by hand with 2.0, 2.6, 3.25 g fresh pasture residues in a 55 ml plastic container, amounting to a concentration of 5.11, 6.81 and 8.51mg C g-1 oven-dried soil or 12, 16 and 20 Mg dry matter residue ha-1 to 10 cm depth, respectively. A control treatment with no residue was also included. All the containers were placed in 1.8L Agee jars containing vials with 10ml 1M NaOH to absorb CO2 and 10 ml CO2 free water to avoid any loss of moisture during incubation. The jars were placed in a room where temperature ranged between 18.5 and 24.3o C during the measurement period (see Figures 5.1 & 5.2). The incubations were carried out for 129 days during which vials of 1M NaOH were removed at 2-4 day intervals for measuring trapped CO2, the jars were flushed with ambient air, a fresh vial containing 10 ml of 1M NaOH was placed back inside the jar for CO2 absorption, and the jars were resealed. The weight of the container + soil + residues was recorded and adjusted for moisture in order to maintain 80% field capacity. Moisture was adjusted weekly to correct the moisture loss from soils during venting of jars. The percentage of C decomposition of the residue was calculated as follows: net CO2-Cemitted = CO2-Camended ? CO2-Ccontrol ??????..(Eq. 5.1) Net CO2-Cemitted represents the amount of C emitted from ryegrass residue amended soil (CO2-Camended) minus C emitted from unamended soil (CO2-Ccontrol), which represents background soil organic matter decomposition. % residue C decomposition= [net CO2-Cemitted/C added] X 100????.. (Eq. 5.2) C added is the amount of C added as residues to soil. 137 It is acknowledged that this calculation does not consider any priming effect caused by addition of the residue. The estimates of residue CO2-C emissions could be overestimated if priming occurs. 5.2.4 Soil fractionation At the end of the decomposition study the control and residue amended soils were then subjected to physical fractionation to see if there were any changes in the fractions due to added residues. 5.2.4.1 Light (LF) and heavy fraction (HF) density separation Two replicates of each treatment (12, 16 and 20 Mg dry matter residue ha-1 and unamended control soil) were separated using density fractionation. Density fractionation was conducted using air-dried samples. Soil subsamples (equivalent to 5 g on an oven dry basis) were weighed into 40 ml centrifuge tubes and 30 ml of NaI (density= 1.8 g cm-3) was added. The tubes were shaken end over end for 5 minutes and were left to stand for 2 hours. After standing, the top half of the suspended soil and floating roots were sucked out of the tube with a rubber hose attached to a side arm flask (under vacuum). A glass tube with 54 ?m nylon mesh fitted at the top was placed inside the flask for collection of the light fraction. The light fraction material collected on the mesh was washed thoroughly to remove any soil particles and NaI solution. The light and heavy fractions were washed once with 0.01M CaCl2 and about 10 times with deionised water as stated by Zhang et al. (2007) to ensure complete removal of NaI. Both light and heavy fractions were dried at 70oC and analysed for total-C by LECO. 5.2.5 Labile carbon fractions 5.2.5.1 Hot water extractable-C Hot water extractable-C (HWC) was determined on field moist soil samples maintained at 80% of field capacity according to the method of Ghani et al. (2003). Soil samples (equivalent 3 g on oven dry basis) were weighed into 50 ml polypropylene centrifuge tubes, and 30 ml of distilled deionised water was added into the centrifuge tubes. These tubes were shaken on a vortex shaker for 10 seconds to suspend the soil in water. The tubes were capped and left for 16 hours in a hot-water bath at 80oC. At the end of the 16 hour period, each tube was shaken for 10 seconds on a vortex shaker to ensure that the HWC released from the soil organic matter was fully suspended in the extraction medium. These tubes were then centrifuged for 20 minutes at 3500rpm (2200g). Supernatants were filtered 138 through cellulose nitrate membrane filters and were analysed for C by acid dichromate digestion. 5.2.5.2 Microbial biomass-C Field moist soil samples maintained at 80% of field capacity were analysed for microbial biomass-C by the fumigation extraction method (Vance et al. 1987). Non- fumigated soil samples and samples that had been fumigated with ethanol free CHCl3 were extracted with 0.5M K2SO4 for 30 min (1:5 soil: extractant ratio), filtered and an aliquot was analysed for C by acid dichromate digestion. The C from the fumigated samples minus non- fumigated samples was taken to represent the microbial-C flush and converted to microbial biomass C using the relationship: microbial C= ????? C flush (Saggar et al. 2001). 5.2.5.3 Hydrogen peroxide oxidisable-C Five grams of ring-ground, air-dried soil was weighed into a 110 ml plastic container, a magnetic bar placed in it, and the plastic container placed in a 1L Agee jar, set on a magnetic stirrer. An ambient air sample (t0) was collected immediately after sealing the jars with the lid fitted with rubber septum. Thereafter, 25 ml of 2% H2O2 was added into the plastic container with a 50 ml polypropylene syringe, and the magnetic stirrer was started to stir the soil-peroxide mixture. Gas samples were collected over a period of four hours (at 0, 1, 2, 3 and 4 hours) using a 25 ml polypropylene syringe fitted with a 3-way stopcock, and transferred into 12 ml evacuated vials. These over-pressured gas samples were analysed using a Shimadzu GC- 17A gas chromatograph (GC). Prior to the calculations it was confirmed that there was a linear production of CO2 during the 4 hour period. The amount of C oxidised from 5 g soil by 2.0% hydrogen peroxide was calculated by subtracting the CO2 concentration at t (0) from t (4hour). The resultant CO2 concentration was used for further calculations: mg CO2-C g -1 soil = ((Resultant CO2 Concentration X Volume of Jar X Density of CO2) X 0.2727) (Eq. 5.3) Weight of soil (g) X 1000 Concentration of CO2 obtained from GC = ?L CO2 L -1 Density of CO2= 1.8 ?g ?L -1 at 25oC temperature and 1 atmosphere pressure Volume of Jar= 1L Weight of soil taken= 5 g 0.2727 is the factor to convert CO2 to C 139 The amount of C oxidised after 4 hours was correlated with the amount of CO2-C respired during the 129 day incubation period. 5.2.5.4 Potassium dichromate oxidisable-C This fraction was determined through a method modified from Walkley and Black (1934) (Chan et al. 2001). The method involves oxidation of total organic carbon by 0.4N K2Cr2O7 using 18 N H2SO4. In the standard Walkley and Black method 36N H2SO4 and 1N K2Cr2O7 are used. The organic carbon oxidisable by 0.4N K2Cr2O7 using 18 N H2SO4 was considered to be a very labile C fraction. 5.2.5.5 Potassium sulphate extractable-C (Soluble-C) Field moist soil samples maintained at 80% of field capacity were extracted with 0.5M K2SO4 for 30 min (1:5 soil:extractant ratio), filtered, and an aliquot was analysed for C by acid dichromate digestion. 5.2.6 Recovery percentage of the added residue-C The recovery percentage of the added residue-C was calculated as follows: % recovery = (LF-C+HF-C) after incubation + CO 2-C emitted x100???... (Eq. 5.4) (LF-C+HF-C) before incubation + added residue-C (LF-C+HF-C) after incubation is the sum of total-C determined on LF and HF of control and residue applied treatments of the four test soils after incubation study i.e. on day 129. CO2-C emitted is the amount of residue-C emitted as CO2 during the incubation period. (LF-C+HF-C) before incubation is the sum of total-C determined on LF and HF of control treatment of the four test soils before starting the incubation study i.e. on day zero. Added residue-C is the rate of residue-C addition to the soil i.e. 5.11- 8.51 mg C g-1 soil. 5.2.7 Statistical analysis An analysis of variance using SAS software (9.1) was performed on the results of cumulative CO2-C emissions, light and heavy fraction C recovered at end of incubation, and percentage of added residues decomposed using a General Linear Model (GLM) procedure. Percentage data was log transformed and mean comparisons were made on transformed data. Mean comparisons were done using Fisher?s least significant difference (LSD) at 5% level of significance. The regression coefficients were determined using SAS software (9.1) to determine relationships between total amounts of CO2 respired during the incubation study and different labile-C fractions extracted. All soil replicates were used in the regression analysis. An analysis of covariance (ANCOVA) was performed using Minitab-16 with soil 140 type as covariate, in order to answer the question of whether the extracted labile-C fractions used to predict the CO2 emissions are affected by soil type. Similar to the linear regressions, the ANCOVA used the response variable on the y-axis and the covariate as the independent variable on the x-axis. Full models that included an interaction of covariate with labile-C fraction were performed. The interaction term allows the regression lines to have different slopes. All soil replicates and residue treatments were used in the analysis. An analysis of variance (ANOVA) was performed using Minitab-16 to analyse the effect of residue rates and soil types on labile-C fractions and CO2 respired. 5.3 Results 5.3.1 Soils Soils used in this study significantly varied in total-C and N contents but there were minor differences in texture and pH. 5.3.2 Decomposition of added ryegrass pasture residues The CO2-C emission rates from all four test soils responded rapidly to the residue application. The first measurements (on day-2) showed that the CO2-C emission rates from residue incorporated soils were four to five times than that observed from control soils. In all the four test soils, residue treatments showed large pulses of CO2 in the initial weeks and then decreased gradually over time (Figures.5.1 and 5.2). It is noted that the correction of moisture loss on day-57 resulted in an increase in CO2 emissions for the next measurement. The amounts of CO2-C emitted from Carnarvon SL, Ohakea stony SiL, Ohakea SiL, and Horotiu SiL were 2.57, 5.02, 6.19 & 7.32 mg C g-1 soil; 2.76, 5.40, 5.72 & 7.10 mg C g-1 soil; 3.59, 6.66, 7.60 & 8.31mg C g-1 soil and 3.32, 5.73, 6.62 & 7.39 mg C g-1 soil at 0, 5.11, 6.81 & 8.51 mg C g-1 soil of residue application rates (Figure 5.3) during the 129 days incubation period. Irrespective of the soil type, CO2-C emissions from the residue treated soils were significantly higher than control soils. Despite the different rates of residue addition, the surface soil conditions were similar in all jars and there was no evidence of excessive fungal growth caused by soil type or residue addition. The proportion of applied residue-C emitted as CO2 during 129 day incubation period varied between 43.3 and 60.0%. No significant differences were observed between the amount/proportion of residue-C decomposed as CO2 at each level of residue applied in all the four test soils (Table 5.3). Irrespective of the residue input rates about 25.0-37.7% of the residue-C decomposed during the first 30 days in all the four test soils, followed by slow 141 decomposition (11.1-14.2%) in the next 30 days (31-60 days). Subsequent residue decomposition was very slow with only 6-11% residue-C decomposed in the following 69 days (Table 5.2). There were no clear effects of soil type or texture on the residue-C decomposition during the incubation period (Table 5.3). There were no clear effects of the total-C contents on the amounts of CO2-C emitted from the control treatment of the four test soils (Figure 5.3). 142 (a) Carnarvon SL (b) Ohakea stony SiL Figure.5.1: The CO2-C emission rates from (a) Carnarvon SL and (b) Ohakea stony SiL as influenced by residue application during 129 days of laboratory incubation. 0 5 10 15 20 25 0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0 10 20 30 40 50 60 70 80 90 10 0 11 0 12 0 13 0 Temperature ( o C) mg CO 2-C g -1 soil d -1 D u rat i o n of incub a t i o n (Days ) Control 5 .1 1 mg C g-1 soil 6.81 mg C g-1 soil 8.51 mg C g-1 soil Temperature (oC) 0 5 10 15 20 25 0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0 10 20 30 40 50 60 70 80 90 10 0 11 0 12 0 13 0 Temperature ( o C) mg CO 2-C g -1 soil d -1 D u rat i o n of incub a t i o n (Days ) Cont rol 5 .1 1 mg C g-1 soil 6.8 1 mg C g-1 soil 8.5 1 mg C g-1 soil Temperature (oC) 143 (a) Ohakea SiL (b) Horotiu SiL Figure.5.2: The CO2-C emission rates from (a) Ohakea SiL and (b) Horotiu SiL as influenced by residue application during 129 days of laboratory incubation. 0 5 10 15 20 25 0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Temperature ( o C) mg CO 2-C g -1 soil d -1 D u rat i o n of incub a t i o n (Days ) Cont rol 5 .1 1 mg C g-1 soil 6.8 1 mg C g-1 soil 8.5 1 mg C g-1 soil Te mpe r a tu r e (oC) 0 5 10 15 20 25 0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0 10 20 30 40 50 60 70 80 90 10 0 11 0 12 0 13 0 Temperature ( o C) mg CO 2-C g -1 soil d -1 D u r a t i o n of incub a t i o n (Days ) Cont rol 5 .1 1 mg C g-1 soil 6.8 1 mg C g-1 soil 8.5 1 mg C g-1 soil Temperature (oC) 144 Figure.5.3: Cumulative CO2-C emissions from Carnarvon SL, Ohakea stony SiL, Ohakea SiL and Horotiu SiL. Each bar represents a mean of four replicates with standard error. Letters indicate statistically significant differences (p < 0.05). 0. 0 1. 0 2. 0 3. 0 4. 0 5. 0 6. 0 7. 0 8. 0 9. 0 Ca rn arvo n SL Oha ke a ston y SiL O ha ke a SiL Ho ro t iu Si L mg CO 2-C g -1 soil Control 5.11 mg C g-1 soil 6.81 mg C g-1 soil 8.51 mg C g-1 soil a b c d a b b d a b c d a b c d 145 Table 5.2: Percentage of added residue-C respired at different times of the incubation in the four different soils amended with different rates of ryegrass pasture residues. SOILS Rates of residue addition 5.11 mg C g-1 soil 6.81 mg C g-1 soil 8.51 mg C g-1 soil 0-30 days Carnarvon SL 28.0 33.6 32.1 Ohakea stony SiL 32.5 25.0 30.7 Ohakea SiL 37.7 34.4 33.9 Horotiu SiL 27.8 27.7 26.2 31-60 days Carnarvon SL 11.1 12.6 12.5 Ohakea stony SiL 12.6 11.9 13.3 Ohakea SiL 14.2 13.3 13.4 Horotiu SiL 12.1 12.3 13.3 61-129 days Carnarvon SL 8.7 6.9 11.0 Ohakea stony SiL 6.4 6.4 6.9 Ohakea SiL 8.0 11.0 8.0 Horotiu SiL 7.2 8.2 8.2 Each value represents a mean of four replicates. Table 5.3: Percentage of added residue-C decomposed after 129 days in the four different soils amended with different rates of ryegrass pasture residue. SOILS Rates of residue addition 5.11 mg C g-1 soil 6.81 mg C g-1 soil 8.51 mg C g-1 soil Carnarvon SL B47.9a A53.2a A55.8a Ohakea stony SiL BA51.6a B43.3a B50.9a Ohakea SiL A60.0a A58.8a A55.4a Horotiu SiL B47.2a BA48.4a B47.8a Each value represents a mean of four replicates. Capital letters (A, B, C, D) stands for differences between soils and small letters (a, b, c, d) stand for differences between residue rates. 146 5.3.3 Residue-C recovery Total-C determined on the light (LF) and heavy fractions (HF), plus the CO2-C emitted during the incubation period (Table 5.4), accounted for 97 to 110% of the total C in residue treatments (at 5.11, 6.81 and 8.51 mg C g-1 soil) and 99 to 105% of C in control treatments. Addition of ryegrass pasture residues had no significant effect on the amounts of C in the heavy fraction in all the test soils during the incubation period, except Carnarvon SL in which the amount of C in the heavy fraction at the 5.11 and 6.81 mg C g-1 soil residue addition significantly decreased in comparison to the control. However, no differences were observed between the control and the 8.51 mg C g-1 soil residue level. In contrast, LF-C showed significant differences between the treatments in all the four test soils (Table 5.4). The total-C determined on the light (LF) and heavy fractions (HF) on the control treatment of the four test soils at the beginning (on day-0) and after 129 days incubation study are given in Table 5.5. No significant differences were observed for LF and HF-C determined at the beginning and after the incubation for all the soils tested. However, over the duration of the incubation study, LF-C of the four test soils declined. 147 Table 5.4: Light and heavy fraction-C as influenced by ryegrass pasture residue addition and recovery (%age) of added C after 129 days of the incubation study. Carnarvon SL Treatment LF-C (mg C g-1 soil) HF-C (mg C g-1 soil) CO2-C respired (mg C g-1 soil) % recovery Control 3.68 ? 0.29 38.53 ? 0.10 2.57 ? 0.09 102.3 5.11 mg C g-1 soil 7.44 ? 0.15 35.31 ? 0.42 5.02 ? 0.10 97.7 6.81 mg C g-1 soil 9.21 ? 0.70 35.53 ? 0.54 6.19 ? 0.11 100.7 8.51 mg C g-1 soil 10.55 ? 2.16 39.88 ? 1.31 7.32 ? 0.10 110.4 LSD (p< 0.05) 4.51 2.91 0.30 Ohakea stony SiL Treatment LF-C (mg C g-1 soil) HF-C (mg C g-1 soil) CO2-C respired (mg C g-1 soil) % recovery Control 0.84 ? 0.05 25.14 ? 0.30 2.76 ? 0.08 99.9 5.11 mg C g-1 soil 3.69 ? 0.50 26.27 ? 0.71 5.40 ? 0.08 104.3 6.81 mg C g-1 soil 2.63 ? 0.82 26.59 ? 0.77 5.72 ? 0.24 98.2 8.51 mg C g-1 soil 4.27 ? 1.04 26.10 ? 0.79 7.10 ? 0.03 100.5 LSD (p< 0.05) 2.78 2.64 0.41 Ohakea SiL Treatment LF-C (mg C g-1 soil) HF-C (mg C g-1 soil) CO2-C respired (mg C g-1 soil) % recovery Control 2.62 ? 0.48 33.23 ? 0.80 3.59 ? 0.14 102.1 5.11 mg C g-1 soil 5.57 ? 0.55 34.02 ? 0.64 6.66 ? 0.14 105.8 6.81 mg C g-1 soil 6.39 ? 1.20 33.04 ? 1.29 7.60 ? 0.03 103.5 8.51 mg C g-1 soil 6.84 ? 0.15 32.92 ? 1.08 8.31 ? 0.08 102.0 LSD (p< 0.05) 2.76 3.86 0.34 Horotiu SiL Treatment LF-C (mg C g-1 soil) HF-C (mg C g-1 soil) CO2-C respired (mg C g-1 soil) % recovery Control 1.94 ? 0.42 47.81 ? 0.97 3.32 ? 0.06 105.5 5.11 mg C g-1 soil 5.95 ? 0.60 46.22 ? 0.92 5.73 ? 0.11 104.5 6.81 mg C g-1 soil 6.95 ? 0.78 46.86 ? 1.63 6.62 ? 0.08 105.8 8.51 mg C g-1 soil 7.64 ? 0.11 45.86 ? 1.80 7.39 ? 0.04 103.5 LSD (p< 0.05) 2.12 5.45 0.23 Each value of LF-C and HF-C represents a mean of two replicates? SE. Each value of CO2-C represents a mean of four replicates? SE. 14 8 T a b le 5 .5 : L ig ht a nd h ea vy f ra ct io n- C a t t he b eg in ni ng ( da y- 0) a nd a ft er 1 29 d ay s in cu ba tio n st ud y in c on tr ol tr ea tm en ts o f th e fo ur s oi ls . E ac h va lu e re pr es en ts a m ea n of f ou r re pl ic at es ? SE f or D ay -0 E ac h va lu e re pr es en ts a m ea n of tw o re pl ic at es ? SE f or D ay -1 29 C ar na rv on S L O ha ke a st on y Si L O ha ke a Si L H or ot iu S iL L F- C (m g C g -1 so il) H F- C (m g C g -1 s oi l) L F- C (m g C g -1 so il) H F- C (m g C g -1 s oi l) L F- C (m g C g -1 so il) H F- C (m g C g -1 s oi l) L F- C (m g C g -1 so il) H F- C (m g C g -1 s oi l) D ay -0 5. 18 ?0 .3 6 38 .6 0? 1. 72 1. 90 ?0 .8 0 26 .8 8? 1. 02 4. 10 ?1 .0 6 34 .5 2? 1. 64 3. 23 ?0 .5 0 47 .0 9? 3. 52 D ay -1 29 3. 68 ?0 .2 9 38 .5 3? 0. 10 0. 84 ?0 .0 5 25 .1 4? 0. 30 2. 62 ?0 .4 8 33 .2 3? 0. 80 1. 94 ?0 .4 2 47 .8 1? 0. 97 p va lu es 0. 06 0. 97 0. 28 0. 19 0. 27 0. 52 0. 09 0. 86 148 149 5.3.4 Labile carbon fractions The purpose of the decomposition study was to assess background decomposition and that of the added pasture residues through emissions of CO2; and relate these emissions to measured labile C fractions. The five labile C fractions determined on the soils used in the study were hot water extractable-C (HWC), microbial biomass-C (MBC), 2% hydrogen peroxide oxidisable-C (H2O2-C), 0.4N potassium dichromate oxidisable-C (K2Cr2O7-C) and 0.5M K2SO4 extractable-C (K2SO4-C). The labile C fractions determined on the control treatments of the four soils used in the study on day-0 i.e. before starting the incubation study are given in Table 5.6. The MBC ranged between 0.35 and 0.93 mg CO2-C g -1soil, HWC between 0.93 and 1.36 mg CO2-C g -1soil, H2O2-C between 0.12 and 0.82 mg CO2-C g -1soil, K2Cr2O7-C between 2.62 and 4.21 mg CO2-C g -1soil and K2SO4-C between 0.10 and 0.21 mg CO2-C g -1soil for the four soils used in the incubation study. Coefficients of determination comparing the CO2-C respired during the 129 days incubation period with the labile C fractions (MBC, HWC, H2O2-C, K2Cr2O7-C and K2SO4- C are given in Table 5.7 A. None of the labile C fractions, except MBC (R2= 0.70**) showed any significant relationship with the CO2-C respired during the incubation period. This suggested an influence of soil type on the extracted labile C fractions used for predicting CO2-C emissions from a range of soils. Listed in Table 5.7 B are the p-values for the slope comparisons of different labile C fractions extracted from the four soil types. Statistically significant differences between the slopes of the four soil types suggested the effect of soil type on the extracted labile C fractions used to predict CO2-C emissions. Table 5.6: Labile C fractions (mg C g-1 soil) determined on the control treatments of the four soils at the beginning (day-0) of the incubation study. Labile C fractions Carnarvon SL Ohakea stony SiL Ohakea SiL Horotiu SiL (mg C g-1 soil) MBC 0.35?0.02 0.48?0.01 0.93?0.03 0.58?0.01 HWC 1.31?0.01 0.93?0.01 1.36?0.02 1.05?0.02 H2O2-C 0.82?0.045 0.20?0.003 0.61?0.024 0.12?0.003 K2Cr2O7-C 3.97?0.03 2.62?0.05 3.52?0.03 4.21?0.03 K2SO4-C 0.16?0.01 0.11?0.00 0.10?0.01 0.21?0.00 Each value represents a mean of four replicates? SE. 150 Table 5.7 A: Linear regression coefficients of determination comparing the CO2-C respired during 129 days incubation period with the labile C fractions determined on the control treatments of the four soils at the beginning (day-0) of the incubation study. Table 5.7 B: P-values comparing the slopes for different labile-C fractions of Carnarvon SL, Ohakea stony SiL, Ohakea SiL with the slope of Horotiu SiL. Soil type Horotiu SiL (p-value for slope) Labile-C fractions MBC HWC H2O2-C K2Cr2O7-C K2SO4-C Carnarvon SL 0.023 0.242 0.041 0.044 0.012 Ohakea stony SiL 0.315 0.018 0.995 0.720 0.818 Ohakea SiL 1.000 0.049 0.029 0.032 0.059 MBC= Microbial biomass-C HWC= Hot water extractable-C H2O2-C= Carbon oxidised by 2% hydrogen peroxide during 4 hour duration K2Cr2O7-C= Carbon oxidised by 0.4N potassium dichromate K2SO4-C= 0.5M K2SO4 extractable-C Of all the labile C fractions determined on the control soils, only MBC and HWC were determined on residue treated soils (residues applied at 0, 5.11, 6.81 and 8.51 mg C g-1 soil) before and after the incubation study to find a relationship between CO2-C evolved during the incubation and MBC & HWC. The MBC and HWC were determined immediately after the addition of residues to the soils. The HWC in the four soils at different residue input rates before and after the incubation study are given in Tables 5.8 and 5.9. Addition of residues significantly increased the HWC content at every level in all the soils before incubation, suggesting that addition of residues also contribute to HWC fraction. There was a decrease in HWC content after 129 days of incubation at every level of residue addition in comparison to the content present in the beginning. Similar results were observed for MBC in all the four soils at different residue input rates before and after the incubation study (Table 5.10 and 5.11). Before incubation, MBC significantly increased in comparison to the control treatment with addition of residues in all the soils. After 129 days of incubation no significant difference was observed between any rates of added residues. Labile C fractions vs. CO2-C respired MBC R2= 0.70** p<0.001 HWC R2= 0.06 p=0.34 H2O2-C R 2= 0.04 p=0.44 K2Cr2O7-C R 2= 0.05 p=0.42 K2SO4-C R 2= 0.01 p=0.75 151 Table 5.8: Hot water extractable-C at the beginning (day-0) of the incubation study in the four soils as influenced by pasture residue additions. HWC (mg C g-1 soil) Treatment Carnarvon SL Ohakea stony SiL Ohakea SiL Horotiu SiL Control 1.31 ? 0.01 0.93 ? 0.01 1.36 ? 0.02 1.05 ? 0.02 5.11 mg C g-1 soil 1.58 ? 0.01 1.17 ? 0.01 1.48 ? 0.02 1.17 ? 0.02 6.81 mg C g-1 soil 1.66 ? 0.02 1.24 ? 0.02 1.53 ? 0.01 1.24 ? 0.02 8.51 mg C g-1 soil 1.70 ? 0.01 1.29 ? 0.01 1.59 ? 0.01 1.26 ? 0.02 LSD (p< 0.05) 0.04 0.04 0.05 0.06 Each value represents a mean of four replicates? SE. Table 5.9: Hot water extractable-C after 129 days of the incubation study in the four soils as influenced by pasture residue additions. HWC (mg C g-1 soil) Treatment Carnarvon SL Ohakea stony SiL Ohakea SiL Horotiu SiL Control 1.09 ? 0.02 0.78 ? 0.01 1.09 ? 0.01 0.80 ? 0.02 5.11 mg C g-1 soil 1.17 ? 0.01 0.84 ? 0.01 1.10 ? 0.01 0.82 ? 0.02 6.81 mg C g-1 soil 1.19 ? 0.02 0.82 ? 0.01 1.12 ? 0.01 0.83 ? 0.02 8.51 mg C g-1 soil 1.22 ? 0.04 0.88 ? 0.02 1.14 ? 0.03 0.83 ? 0.03 LSD (p< 0.05) 0.09 0.06 0.07 0.09 Each value represents a mean of two replicates? SE. 152 Table 5.10: Microbial biomass-C at the beginning (day-0) of the incubation study in the four soils as influenced by pasture residue additions. MBC (mg C g-1 soil) Treatment Carnarvon SL Ohakea stony SiL Ohakea SiL Horotiu SiL Control 0.35?0.02 0.48?0.01 0.93?0.03 0.58?0.01 5.10 mg C g-1 soil 0.50?0.04 0.65?0.01 1.04?0.00 0.71?0.02 6.81 mg C g-1 soil 0.53?0.03 0.73?0.02 1.11?0.02 0.74?0.02 8.51 mg C g-1 soil 0.59?0.04 0.75?0.04 1.11?0.02 0.75?0.02 LSD (p< 0.05) 0.11 0.07 0.06 0.05 Each value represents a mean of four replicates? SE. Table 5.11: Microbial biomass-C after 129 days of the incubation study in the four soils as influenced by pasture residue additions. MBC (mg C g-1 soil) Treatment Carnarvon SL Ohakea stony SiL Ohakea SiL Horotiu SiL Control 0.23?0.01 0.37?0.01 0.38?0.00 0.33?0.03 5.10 mg C g-1 soil 0.24?0.03 0.38?0.00 0.37?0.02 0.35?0.06 6.81 mg C g-1 soil 0.27?0.01 0.40?0.00 0.40?0.00 0.35?0.03 8.51 mg C g-1 soil 0.27?0.03 0.47?0.05 0.40?0.01 0.39?0.00 LSD (p< 0.05) 0.09 0.11 0.04 0.16 Each value represents a mean of two replicates? SE. The plots of the relationship between cumulative amount of CO2-C emitted and MBC and HWC measured at the beginning (day-0) of the incubation study using ANCOVA are given in Figures 5.4 (a, b) and p-values comparing the slopes for four soil types in Table 5.12. Analysis of covariance takes different soil types into account and explains more variation in the data i.e. MBC (R2= 0.75) and HWC (R2= 0.89) and showed a reasonable relationship with cumulative amount of CO2-C evolved during the incubation study. However, the slopes of the four soil types (Table 5.12) differ significantly, which suggested the effect of soil type on the extracted labile C fractions. 153 (a) (b) Figure.5.4: Relationships between cumulative CO2-C respired during 129 days incubation from the four soil types at different residue rates with (a) microbial biomass-C and (b) hot water extractable-C measured at the beginning (day-0) of the incubation study using ANCOVA model. Table 5.12: P-values comparing the slopes of the Carnarvon SL, Ohakea stony SiL, Ohakea SiL with the slope of Horotiu SiL for microbial biomass-C (MBC) and hot water extractable- C (HWC) determined at different residue rates. Soil type Horotiu SiL (p-value for slope) MBC HWC Carnarvon SL 0.164 0.000 Ohakea stony SiL 0.042 0.001 Ohakea SiL 0.074 0.000 Table 5.13: Summary of the analysis of variance of the amounts of microbial biomass-C (MBC) and hot water extractable-C (HWC) determined on day-0 when four soils were incubated with different ryegrass residue rates and the total CO2-C evolved during the incubation. Source DF Sum of squares MBC HWC Total CO2-C evolved Soil type 3 2.6408 2.1042 17.534 Residue rate 3 0.4542 0.8285 176.054 Soil type X Residue rate 9 0.0223 0.0671 2.193 Error 48 0.1162 0.0439 2.196 Total 63 3.2337 3.0438 197.978 0. 0 1. 0 2. 0 3. 0 4. 0 5. 0 6. 0 7. 0 8. 0 9. 0 0. 0 0. 5 1. 0 1. 5 2. 0 Cummulative CO 2-C respired (mg CO 2-C g -1 soil) M BC (m g C g -1 soil ) C a r n ar v on SL Oh a ke a sto ny SiL Oh a ke a SiL H o r o ti u SiL R 2 =0. 75 0.0 1. 0 2. 0 3. 0 4. 0 5. 0 6. 0 7. 0 8. 0 9. 0 0. 0 0. 5 1. 0 1. 5 2. 0 Cummulative CO 2-C respired (mg CO 2-C g -1 soil) HW C (m g C g -1 soi l ) C a r n arv on SL Oh a ke a sto ny SiL Oh a ke a SiL Ho r o ti u SiL R 2 =0. 89 154 In order to check the capability of H2O2 to oxidise the labile-C fraction from the soils used in this study, all the four test soils (5 g ring ground, air dried soil) were mixed with glucose (a highly labile C source) equivalent to 44.12 mg C (the amount was calculated on the basis of the amount completely oxidised by 25 ml of 1% H2O2), and ryegrass pasture residues equivalent to 35.31 mg C (amount equivalent to the 12 Mg residues per hectare treatment used in the laboratory decomposition). Glucose and ryegrass pasture residue mixed soil samples (in triplicate) and were then oxidised with 2% H2O2 at 35 oC for 2 hours (Table 5.14). The amounts of C oxidised from soils with addition of glucose and residues did not show any significant difference in comparison to the amounts oxidised from the control soils. The amount of carbon oxidised by 2% H2O2 showed a similar trend as observed when soils alone were oxidised, suggesting that the oxidation behaviour of H2O2 is affected by the soil type and might not be able to differentiate between residue inputs. Table 5.14: Amount of carbon oxidised by 2% H2O2 during a 2 hour oxidation period at 35oC from the four soils. Treatment Carnarvon SL Ohakea stony SiL Ohakea SiL Horotiu SiL (mg C g-1 soil) Soil alone 0.66?0.06 0.27?0.04 0.60?0.05 0.22?0.07 Soil+glucose 0.69?0.11 0.29?0.07 0.59?0.12 0.18?0.06 Soil+roots 0.67?0.07 0.32?0.04 0.64?0.09 0.29?0.07 LSD (p< 0.05) 0.30 0.19 0.33 0.24 Each value represents a mean of three replicates? SE. 155 5.4 Discussion 5.4.1 Decomposition of added ryegrass pasture residues Carbon dioxide evolution from soils responded rapidly to residue incorporation. The pattern of decomposition suggested an initial fast phase due to mineralization of easily degradable organic C in residues, followed by a slower phase in which more complex and recalcitrant forms or transformed metabolites are mineralized (Table 5.2). This pattern is supported by many previous studies (Saggar et al. 1996; Curtin et al. 1998; Lu et al. 2003; Nourbakhsh 2006; Verma et al. 2010). During the 129 days of incubation, the proportion of residues decomposed did not differ between differences in soil texture/type as no consistent trend was observed (Table 5.3). Numerous studies conducted in the past to determine the influence of soil texture (clay content) on organic residue decomposition (Sorensen 1983; Ladd et al. 1981, 1985, 1992; Sharkov and Bukreeva 2004) have showed that in comparison to sandy soils, fine textured soils (with higher clay content) have lower rates of residue decomposition and retain higher proportions of residues by complexing with decomposition products, thereby reducing the losses of residue C from soil. The results of the present study are not in accordance with the studies reported in the literature where greater differences in soil textures (sand/clay soils) have been compared, contrasting with the narrow range of soil textures (sandy loam/silt loam) and the small number of soils used in this study. Results of this study showed that the proportion of added residue C decomposed during the entire incubation was independent of the rates of residue addition. These results are in accordance with the results obtained by Jenkinson (1977), who suggested that ?the proportion of the C evolved from residue amended soil above from the control soil has always been independent of the quantity added if the C addition does not exceed 1.5% of the dry weight of soil and if decomposition was allowed to continue for at least 3 to 6 months?. These conditions are applicable to our study as the experiment was conducted over 4 months and residue C additions did not exceed 1.5% of the dry weight of soil. 5.4.2 Residue-C recovery Christensen (1992) reported that the LF is composed of mineral free organic residues at various degrees of decomposition with high C concentration, whereas the heavy fraction (HF) comprises high density organo-mineral complexes with low C concentrations that are more stable than the light fraction. Separation of the whole soil into a light (< 1.8 g cm-3) and heavy fraction (> 1.8 g cm-3) showed that the HF-C was completely unaffected by the application of residue (Table 5.4). This suggested that the HF consisted of stable/native C 156 with a very low decomposition rate. The significant decrease of the HF-C at 5.11 and 6.81 mg C g-1 soil residue rates in Carnarvon SL in comparison to the control could be due to experimental variability. However, the LF-C showed clear differences between residue application rates, and all the applied residue rates showed a typical decomposition pattern - initially rapid followed by slower decay rates. The results of the present study are similar to that reported by Magid et al. (1997). They separated the residues remaining in the soils into light and heavy fractions after a 20 month incubation period and observed that application of residues made no difference to HF-C, whereas LF-C increased with application of residues. The amounts of LF-C in the control treatment of the four soils at the beginning and after 129 days incubation did not vary significantly (Table 5.5). However, over the duration of the incubation study, LF-C of the four test soils decreased. The large replicate variability due to presence of the background root residues in the 2 mm sieved soils used in the study could explain why significant differences were not observed. 5.4.3 Labile carbon fractions Many chemical and biological techniques have been proposed to measure the labile fraction of soil organic matter in a hope to provide an index of loss of organic carbon due to land use changes. Among the various labile C fractions, hot water extractable-C (HWC), microbial biomass-C (MBC), 2% hydrogen peroxide oxidisable-C (H2O2-C), 0.4N potassium dichromate oxidisable-C (K2Cr2O7-C) and 0.5M K2SO4 extractable-C (K2SO4-C) were used to predict the CO2-C evolved during the laboratory incubation. Except for MBC, all other labile C fractions (i.e. HWC, H2O2-C, K2Cr2O7-C and K2SO4-C) had poor and insignificant relationships with the amount of CO2-C respired during the incubation study from the control soils (Table 5.7A). The significant differences between the slopes suggest that the labile C fractions were strongly influenced by soil type (Table 5.7B). Soil treatment with hydrogen peroxide was introduced by Robinson (1922) to remove organic matter prior to particle size analysis and before soil mineralogy analysis (Feller et al. 1992). The SOC fraction resistant to H2O2 treatment is considered to be a stable C fraction (Leifeld and Kogel-Knabner 2001). Many past studies (von Lutzow et al. 2007; Helfrich et al. 2007; Favilli et al. 2008) using concentrated H2O2 (10-30% (wt/wt)) suggested wet oxidation with H2O2 selectively removes the active soil C pool, leaving behind the stable C pool. In the present study a very mild concentration of H2O2 (2% (wt/wt)) was used to oxidise the labile-C fraction. Among the four soils, the amount of carbon oxidised by H2O2 157 was highest in Carnarvon SL followed by Ohakea SiL, Ohakea stony SiL, and lowest in Horotiu SiL (Table 5.6), which could be due the influence of clay content on the oxidation of soil C by H2O2. Hosking (1932) observed that soils having a higher clay content showed resistance to oxidation by H2O2, signifying the protection of organic matter by interaction with clay minerals. During H2O2 oxidation, Fe and Al associated with organic matter are released and precipitate as hydroxides on organic and inorganic components. These precipitates alter the specific surface area and charge of the mineral phase and can protect organic matter from further degradation (Sequi and Aringhieri 1977). This could explain why the lowest amount of C oxidised was observed in the allophanic Horotiu SiL soil (rich in clay minerals of short-range order namely allophane, ferrihydrite and imogolite). Eusterhues et al. (2005) observed a positive correlation between the peroxide resistant organic C with clay content and iron oxides in two forest soils. Petigara et al. (2002) observed that in surface soils with high organic matter and manganese content, H2O2 decomposes to water, di-oxygen and hydroxyl ion instantly, with water and di-oxygen dominating the decomposition, and the formation of free hydroxyl ion represents <10% of the total H2O2 decomposed. The consumption of H2O2 in metal oxidation reduces organic matter oxidation. Hot water extractable-C consists of a labile pool of SOM which includes microbial biomass C, carbohydrates, and amines (Sparling et al. 1998). Previous studies (Ghani et al. 2003; Schulz 2004) suggested hot water extractable-C as one of the sensitive indicators to reflect changes in soil organic matter caused by different management practices. Ghani et al. (1999) observed that 45-60% of C extractable with hot water was carbohydrates. Shepherd et al. (2001) and Haynes et al. (1991) showed a decrease in hot-water extractable carbohydrate in pasture soils on conversion to continuous cropping using conventional cultivation. Significant increases in the HWC fraction, with addition of pasture residues in comparison to the control treatment in all the four test soils before the incubation, suggest the contribution of residues to the HWC fraction. Soil microbial biomass C is an active C fraction used as an early predictor of change in soil organic matter due to land use practices (Powlson et al. 1987; Yeates and Saggar 1998; Saggar et al. 2001; Melero et al. 2006; Brookes et al. 2008; Nyamadzawo et al. 2009). Addition of pasture residues significantly increased the MBC in comparison to the control treatment in all the test soils before the incubation study. These results accord with the previous studies which suggest that soil MBC is affected by the input of organic residues, with high amounts of organic inputs resulting in higher microbial biomass carbon (Peacock 158 et al. 2001; de Araujo and de Melo 2010). In the present study, MBC measurements were made immediately after the addition of residues. However, previous studies (Ocio and Brookes 1990; Ocio et al. 1991a, b) suggest that, to have a valid estimate of the biomass C in residue-amended soils, measurements should be made 5-20 days after the addition of residues. Due to its rapid turnover rate, microbial biomass responds quickly to land use changes in comparison to total SOM, but some researchers regard it as a poor predictor of SOM change in the longer term because the amount of MBC in soil is affected by the amount of crop residues, soil moisture, and temperature (Wardle and Parkinson 1990; Sarathchandra et al. 1989; He et al. 1997; Murphy et al. 2007). Soil C oxidised by 0.4N K2Cr2O7 was assumed to be the labile C fraction. The amount of C oxidised by 0.4N K2Cr2O7 ranged between 8.3-9.2% of the total-C content present in the four test soils used in the study. This suggested that 0.4N K2Cr2O7 oxidises a constant proportion of total-C depending upon the soil type, therefore 0.4N K2Cr2O7 oxidisable-C fraction cannot be regarded as a labile C fraction. This could explain why a poor relationship was observed between K2Cr2O7-C and the amount of CO2 respired from the control soils. Soil C extracted by 0.5M K2SO4 was assumed to be a soluble C fraction, however, a weak and insignificant relationship (R2=0.01) was observed between K2SO4-C and the amount of CO2 respired from the control soils during the 129 days of laboratory incubation. Results of the regression analysis in Table 5.7 A and Figures 5.4 a and b show that neither MBC or HWC labile-C fractions could be used to predict CO2 emissions from different soils due to management practices, or in experimental incubation of four soils with different ryegrass residue additions. This result contrasts with other studies (Ghani et al. 2003; Melero et al. 2006), which have shown that these labile-C pools could be used as sensitive indicators of soil-C change due to contrasting tillage practices and/or different cropping systems over significant periods of time on the same soil. To explain why the labile-C tests fail to predict the soil CO2-C emissions due to management practices it is instructive to consider the analysis of variance results for both the amounts of carbon extracted by the two tests, MBC and HWC, and for the total CO2-C evolved from the experimental incubation of ryegrass residues in four different soil types. In this study the amounts of labile-C measured by MBC and HWC were strongly influenced by soil type, which explained 69.1 and 81.6% of the variation respectively, and were less influenced by the amount of ryegrass residues, which only accounted for 14.0 and 27.2% of the variance, respectively (Table 5.13). However, emissions of CO2-C from these soils were 159 more strongly affected by the amount of residue added (Figure 5.3) explaining 88.9 % of the variation in total CO2-C emissions, whilst soil type only explained 8.8% (Table 5.13). This difference in the relative influence of soil type and residue rates on labile-C fractions and CO2-C emissions explains why the labile-C fractions were poor indicators of CO2-C emissions. 5.5 Summary and conclusions Laboratory incubation was conducted to assess the differences in the decomposition of ryegrass pasture residues in different soil types. Four soils (Carnarvon SL, Ohakea stony SiL, Ohakea SiL and an allophanic Horotiu SiL) received ryegrass pasture residues at 0, 5.11, 6.81, and 8.51 mg C g-1 oven dried soil, equivalent to 0, 12, 16 and 20 Mg oven-dried residues ha-1 and incubated in 1.8L Agee jars at 80% of field capacity for about a 4 month period. The amount of ryegrass pasture residues decomposed was estimated from the amount of CO2 emitted during the decomposition. Over the first 60 days of incubation, ryegrass pasture residues were rapidly mineralised in all the soils and subsequent decomposition was slow. In this study, the proportion of ryegrass residues decomposed did not differ between differences in the soil texture/type and were independent of the rates of residue addition. An investigation to correlate the labile C fractions with the CO2-C respired was conducted during this incubation period. Labile C fractions (HWC, MBC, H2O2-C, K2Cr2O7- C and K2SO4-C) were determined on day zero before beginning the incubation study to correlate these fractions with the amount of CO2-C evolved from the soils during 129 days? incubation period. None of the labile C fractions except MBC showed any significant relationship with the CO2-C respired during incubation of control soils. Results of the present study suggest that it is not possible to derive a single relationship using any of the labile-C fractions to predict CO2 loss from different soil types due to a change in soil management practices. 160 5.6 References Brookes, P. C., Cayuela, M. L., Contin, M., De Nobili, M., Kemmitt, S. 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Soil & Tillage Research, 93(1), 102-108. 165 Chapter-6 Short term test plus development of predictive decomposition simulation model 6.1 Introduction As discussed in Chapter-5, the measured labile soil C fractions (hot water extractable-C, microbial biomass-C, hydrogen peroxide oxidisable-C, potassium dichromate oxidisable-C and potassium sulphate extractab le-C) were not accurate predictors of the amount of CO 2 -C respired from soils during the four month laboratory incubation study. Therefore, it was considered that CO 2 emitted during a short period of incubation may be related to the total amount of CO 2 emitted in both the laboratory incubations and in turn related to the amount of CO 2 emitted in the field. This principle is the same as used to predict the quantity of mineralisable N present in cropped soils from short-term laboratory incubation (Gianello and Bremner 1986). To extend this concept further to predict CO 2 loss from conventional and No-tillage cultivation from laboratory CO 2 emission data a soil C decomposition simulation model is required. Two most commonly used soil organic matter (SOM) decomposition simulation models are Roth-C (Jenkinson 1990) and Century (Parton et al. 1987). These models are designed for large spatial and temporal scales to estimate soil-C dynamics on regional and global scales (year time steps). The concepts used in these models to simulate the decomposition of SOM pools having different ra tes of decomposition has proved effective to model the long term dynamics of soil C (Smith et al. 1997). It is necessary that shorter time step models of SOM decomposition are deve loped and validated under field conditions (Leite et al. 2 0 0 9 ; Lei et al. 2006; Bruun et al. 2003) to simulate short term (hourly or daily) CO 2 variations. In this study, periods of st udy did not exceed 99 days for a season and the variable that was used to valid the model was CO 2 -C flux from a four hour measurement period. It was beyond the scope of this study to attempt to modify either the Roth-C or Century models to simulate pasture and crop residue C decomposition within a season. For this purpose, the carbon pool concepts used in th e longer-term models were used to develop a three compartment model for a short-term simulation of the daily CO 2 emissions observed in the laboratory and field. La Scala Jr et al. (200 9) developed a single compartment model to describe short-term CO 2 losses after tillage, however it was clear at a very early stage of data simulation that a single compartment model could not simulate the very rapid emission 166 of CO 2 in the first 15 days, the slightly reduced rates from 16 to 60 days (Figure 6.3) and the slow rates of emission beyond 60 days. The hypothesis is that the amount of CO 2 emitted from short term laboratory incubation can be used to predict the field soil C loss either by simple regression analysis or by a more complex relationship involving the key factors influencing C loss within the context of a soil C decomposition simulation model. 6.2 Methodology 6.2.1 Simple regression model Carbon dioxide emissions measured under lab oratory conditions from simulated tillage (ST) and No-tillage (NT) in-situ soil cores (Chapter-4 section 4.3.1) were used to predict soil-C loss in field during the summer season. All replicates used to measure CO 2 fluxes under laboratory conditions and in the fiel d were used in the regression analysis. For field comparisons, each replicate used in the laboratory measurements represents the position of the static chamber installed in the field for both the RT and NT treatments. In brief, to compare two ti llage treatments: simulated-ti llage (ST) and No-tillage (NT) and a non-disturbed (ND) control, in-situ soil cores (10 cm diameter, 10 cm depth) were collected at a soil depth of 0-10 cm from 4 different locations on each field site. Non- disturbed (ND ) cores were collected before the start of field tillage operations and the NT treatment cores were collected over the slots immediately after the Cross Slot ? No-tillage drilling. Each treatment comprised four replicat es and each replicate was composed of three intact cores collected from one sampling location. Half of the total numbers of ND cores collected from each field site were broken up in the laboratory to simulate tillage treatment. The ST treatment was formulated from three ND soil cores by emptying the soil, breaking it into pieces, thoroughly mixing and packing it in a plastic container. Moisture contents in the cores and container as sampled from the field were maintained throughout the experiment by weighing the soil cores and spraying the required amount of deionised water onto the surface. Soil in-situ cores and plastic containers filled with soil were placed in closed base static chambers and CO 2 measurements were taken on a daily basis at constant temperature (23 0 C) by placing a plastic petri dish containing 30 ml of 1M NaOH within each chamber for a 4 hour period. Carbon dioxide measurements c ontinued for 92 days (Glen Oroua), 83 days (Tangimoana), 81 days (Kiwitea), 54 days (Feilding) and 99 days (Sanson) until the emissions subsided and the differences between the treatments became negligible. 167 Least squares ? fitting of the CO 2 -C evolved until days 2, 4, 6, 8, 10 and 12 with total amounts of CO 2 -C evolved during the full incubation period were used to determine the duration of incubation. 6.2.2 Two and three compartment decomposition models Tillage induced CO 2 -C fluxes have been shown to be important over short durations (Reicosky and Lindstrom 1993, Rochet te and Angers 1999, La Scala et al. 2001, 2006) and are primarily related to the decay of the labile C fraction which has more rapid turnover than the total soil-C (La Scala Jr et al. 2009). Tillage also changes the soil conditions i.e. improved oxygen, temperature and moisture conten ts required for rapid decomposition (Six et al. 1998). Therefore, to build models that simulate CO 2 emissions in field soils with varying amounts of C in crop residues and more mature SOM, and to eventually accommodate the environmental effects of varying soil temperature and moisture, compartmented model structures based on the principle of the classical five compartment Roth-C model (Jenkinson 1990) were constructed. 6.2.2.1 Development of a temporally dynamic two compartment model using laboratory data In the proposed two compartment dynamic model CO 2 fluxes are expressed in terms of C so they can be directly related to C present in the soil. In the two compartment model, the two compartments are an active crop or pasture residue pool and a more stable, soil C pool. It was assumed that the size of the act ive pool was negligible in comparison to the stable-C pool; therefore, the initial stable-C pool in the two compartment model was sized based on the measured total soil-C. Values for decay constants (for active and stable-C pools) were found by iteratively varying each value to maximize the coefficient of determination between the predicted (modelled) and observed 12 days daily flux values during laboratory incubations. After several iterations using Microsoft Excel 2010 for the five different soil types, standard size of active pool and decay constants for active and stable-C pools were fixed as described: The two compartment model to describe the daily flux rate of CO 2 from tilled soil: Fn = (C n x kc) + (A n x ka) ??????????????????.......???..(Eq. 6.1) Where: F n= Flux rate on any day n (Mg C ha -1 d-1 ) 168 Stable-C pool (Mg C ha -1 ) is represented as C (pool-C), th e initial stable-C pool value was determined by multiplying total-C concentrati on by soil bulk density and its subsequent values i.e. Cn on any day n was determined using the following formula: Cn= [C (n-1) ?(C (n-1) x k c)] The most labile or active-C pool (crop or pasture residues Mg C ha -1 ) is represented as A (pool-A). A n on any day n was determined using the following formula: An= [A (n-1) - (A (n-1) x k a)] The decay constants k c and k a were estimated experimentally ba sed on the best fit of the model to measured CO 2 -C fluxes during the 12 day laboratory incubation of the simulated tillage treatment of each of the five soils used in the laboratory incubation study (Chapter-4). The model was then used to predict emissions for the full term incubations. The initial assumptions were that the pool-C decay constant (k c) was 0.0005 per cent of the initial size of pool-C per day a nd was assumed to be constant for all the soils irrespective of soil type. For example, if the initial size of the stable-C pool for a particular soil is 44.4 Mg ha-1 , it will decay at 0.00022 d -1 . The size of pool-A was based on the amount of C lost as CO 2 during 12 days incubation from disturbed/simulated till ed soils under controlled laboratory conditions. For pool-A, the decay constant (k a) was fixed as 0.10 d -1 and was assumed to be constant for all the soils irrespective of the soil type. For the No-tilla ge treatment the decay constant for pool-A was allowed to vary in the two and three compartment models. The daily decay constant values equate to < 0.2 per cent of the stable-C pool which is similar to the values used in the models calculating annual C change (Bayer et al. 2006; Leite et al. 2009). The fast pool decomposes very quickly and cannot be equated with annual models. The two compartment model does not allow the transfer of C from the stable pool to the labile pool for the period of decomposition. To predict the CO 2 emissions from the decomposition of crop residues over a short period of time it may not be necessary to include the C transfer between pools. Variations to a three compar tment model with and without temperature and moisture are discussed in the Results and Discussion section 6.3 of this chapter. 6.2.2.2 Model efficiency (ME) also known as one of the expressions of R 2 (coefficient of determination) in non-linear fitting evaluations , was calculated by the following formula as stated by La Scala Jr et al. (2009): 169 ?? ? ? ? ? ??? ??? ? ???????????? ? ?????? ? ???????????? Where: ????? is the observed CO 2 -C flux ?????? is the mean of observed CO 2 -C fluxes throughout the measurement period ??????is the predicted CO 2 -C flux Model efficiency/R 2 will vary between minus infinity and 1 with higher values (closer to 1) indicative of superior performance. 6.3 Results and discussion 6.3.1 Simple regression model The least squares regression analysis (Tab le 6.1) showed a significant relationship between the total amounts of CO 2 -C respired during the full incubation period (54 to 99 days) with the CO 2 -C evolved until days 2, 4, 6, 8, 10 and 12. Amongst the days, day-12 showed the highest R-square value and lowest residual standard error compared to day-2, 4, 6, 8 and 10. Therefore, the 12 day laborator y incubation (i.e. th e total amount of CO 2 -C evolved to day-12) was proposed to be a good pr edictor of C lost from the ST and NT soils of the five soil sites used during laboratory incubation. McLauchlan and Hobbie (2004) also suggested 12 days of laboratory incubation as the most accurate technique to estimate labile-C because the majority of the material being mineralized during this period is labile and it closely resembles natural decomposition under field conditions. Moreover, a commercial test ca nnot be longer because it has to provide timely advice to farmers after spraying glyphosate herbicide. Table 6.1: Linear regression coefficients of deermination comparing the CO 2 -C evolved during full laboratory incubation period (54 to 99 days) with CO 2 -C evolved; until day 2, 4, 6, 8, 10 and 12 using soils from five soil sites. Days Simulated tillage (ST) No-tillage (NT) Day-2 R 2 = 0.46* RSE=0.61 R 2 = 0.68* RSE=0.42 Day-4 R 2 = 0.73* RSE=0.43 R 2 = 0.81* RSE=0.32 Day-6 R 2 = 0.85* RSE=0.32 R 2 = 0.88* RSE=0.26 Day-8 R 2 = 0.90* RSE=0.26 R 2 = 0.91* RSE=0.22 Day-10 R 2 = 0.93* RSE=0.22 R 2 = 0.93* RSE=0.19 Day-12 R 2 = 0.96* RSE=0.17 R 2 = 0.94* RSE=0.18 *p<0.001 RSE= residual standard error 170 The linear regression (Figures 6.1 and 6.2) showed a strong relationship (R 2 =0.87) between the total CO 2 -C evolved from simulated tillage (ST) and No-tillage (NT) soils until day-12 with the full laboratory incubation period (54 to 99 days) for the five soil sites. However, a weak relationship (R 2 =0.35) was observed when the total CO 2 -C evolved from simulated tillage (ST) and No-tillage (NT) soils until day-12 under laboratory conditions was compared with CO 2 -C evolved from rotary tillage (RT) and No-tillage (NT) soils for the full measurement period (99 days) during the summer season from the Sanson field site. Separate linear regression models were f itted for the ST and NT treatments for both the laboratory (Figure 6.1) and field (Figure 6.2 ) comparisons and were analysed for the differences of slopes and intercepts between treatments using analysis of covariance. No significant differences were observed for the slopes (p=0.88 for the laboratory (Figure 6.1); p=0.77 for the field comparison (Figure 6.2)) and intercepts (p=0.06 for the laboratory (Figure 6.1); p=0.35 for the field comparis on (Figure 6.2)) between the NT and ST treatments. Therefore it was decided to use a single regression model for both the treatments. Due to variations in field soil moisture and temperature, CO 2 -C emissions observed in the field were higher than observed in laboratory. This resulted in a weak relationship between laboratory and field measurements. The complex process of CO 2 production and release from soils could not be explained without taking into account the soil temperature and moisture content (Wildung et al. 1975). Wagner et al. (1997) observed that a linear regression model underestimates the CO 2 flux rates and suggested developing a model based on the complex physical mechanisms involved because the production of CO 2 in soils is often nonlinear. Therefore, the concept that fiel d soils being brought into the laboratory, cultivation treatment simulated and the CO 2 -C evolved during 12 day incubation being used to predict field soil C loss appears to be unworkable if field soil climate conditions cannot be simulated. For this reason, attention was turne d to testing two or three compartment time dependent models that could be made to respond to temporal climate change. 171 Figure.6.1: The relationship between the total CO 2 -C evolved from simulated tillage and No-tillage soils until day-12 with CO 2 -C evolved during full incubation period (54 to 99 days) from the five soil sites during laboratory incubation study. Figure.6.2: The relationship between the total CO 2 -C evolved from simulated tillage and No-tillage soils until day-12 under laboratory conditions with total CO 2 -C evolved from rotary tillage (RT) and No-tillage (NT) soil s in field for the full measurement period (99 days) during summer for the Sanson field site. y = 3.38x - 0.14 R? = 0.87; p < 0.01 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 To ta l C O 2- C (M g CO 2- C ha -1 ) m ea su re d in la bo ra to ry d ur in g fu ll in cu ba ti on p er io d Total CO2-C (Mg CO2-C ha-1) measured in laboratory until day-12 ST NT y = 1.08x + 2.36 R? = 0.35; p = 0.12 2.5 3.0 3.5 4.0 0.4 0.5 0.6 0.7 0.8 0.9 1.0 To ta l C O 2- C (M g CO 2- C ha -1 ) m ea su re d in fi el d id ur in g 99 d ay s pe ri od Total CO2-C (Mg CO2-C ha-1) measured in laboratory until day-12 RT NT 172 6.3.2 Application of two compartment model to predict CO2 fluxes in laboratory for full measurement period Results of developing a CO 2 -C flux model by fitting th e 12 day laboratory incubation data and then predicting CO 2 -C fluxes for full measurement period (54-99 days) for the five soil sites for simulated tillage (ST) treatment with a two compartment model are presented in Tables 6.2 and 6.3, respectively. The coefficient of determination (R 2 ) for simulated tillage treatment showed good agreement between observed and predicted (modelled) CO 2 -C fluxes for the full measurement period for most of the soil sites (Table 6.3). However, fitting for the full measurement period CO 2 -C fluxes from simulated tillage treatment in the laboratory study showed that the model starts under-estimating the fluxes after a few days suggesting that the simulated flux rates needs to be increased. The predicted (modelled) CO 2 -C fluxes when plotted with observed fluxes for the Glen Oroua site (Figure 6.3 c) shows the under- estimation of predicted fluxes by the two co mpartment model for the simulated tillage treatment; figures for the remaining sites are not shown. Therefore the third pool i.e. the intermediate pool was included in the two compartment model resulting in the three compartment model. The three compartment model to describe the daily flux rate of CO 2 from tilled soil: Fn = (C n x kc) + (A n x ka) + (B n x kb) ???????????...????.?.? (Eq. 6.2) Where: F n= Flux rate on any day n (Mg C ha -1 d-1 ) Pools C & A and their respective decay consta nts are same as described in equation 6.1. Intermediate-C pool (Mg C ha -1 ) is represented as B (pool-B); initial size was set as: Pool-B (Initial) = Pool-A (Initial) x 1.5 and its subsequent values i.e. B n on any day n were determined using the following formula: B n= [B (n-1) - (B (n-1) x k b)] The size and decay constant (k b) of pool-B was estimated expe rimentally based on the best fit of the model to measured CO 2 -C fluxes during the 12 days incubation of the simulated tillage treatment of each of the five soils used in laboratory incubation study. For pool-B, decay constant (k b) was assumed to be 1.0 per cent of initial size of pool-B per day. For example, if the initial size of pool-B for particular soil is 1.22 Mg ha -1 it will decay at 0.0122 d -1 and is assumed to be constant for all the soils irrespective of soil type. 173 A factor of 1.5 used to estimate pool-B from pool -A gave the best fit to the 12 days observed laboratory data. The decay constants were set on the initial pool sizes. Figure 6.4 shows the contribution of pools A, B and C to CO 2 fluxes in two and three compartment models. Inclusion of intermediate pool (pool-B) in the two compartment model (i.e. the three compartment model) better fitted the laboratory data for ST treatment (Figure 6.3 d) and (Tables 6.4 and 6.5). The model was further adjusted by changing th e decay constant of pool-A to predict the No- tillage laboratory data. The three compartment model to describe the daily flux rate of CO 2 from No-tilled soil: Fn = (C n x kc) + (A n x ka/2) + (B n x kb)?????????????.?..?...? (Eq. 6.3) The model parameters described in equation 6.2 for tilled soils were used to predict CO 2 -C fluxes from No-tilled soils only by varying the value of decay constant (k a) of pool- A. The decay constant value (k a) of pool-A was estimated expe rimentally based on the best fit of the model to measured CO 2 -C fluxes during the 12 days ? incubation of the No-tillage treatment of each of the five soils used in the laboratory incubation study. The value of the decay constant (k a) of pool-A to predict the CO 2 -C fluxes from the No-tillage treatment was half the value used to fit the 12 days ? CO 2 -C fluxes observed from simulated tillage treatment in the laboratory. The adjusted three compartment model very well fitted the 12 days and full measurement laboratory data for the NT treatment (Tables 6.4 and 6.5). 174 T a b le 6 .2 : T w o ca rb on p oo ls (P oo l-C a nd A ), t he ir r es pe ct iv e de ca y co ns ta nt s (k c an d k a ) a nd c oe ff ic ie nt o f de te rm in at io n (R 2 ) of t he d ai ly m ea su re d an d pr ed ic te d (m od el le d) C O 2-C f lu xes f or 12 da ys o f th e fi ve s oi ls u se d in la bo ra to ry i nc ub at io n st ud y fo r de ve lo pi ng th e tw o co m pa rt m en t m od el . Soi l s ite s T ill ag e tr ea tm en t Po ol -C (Mg C h a -1 ) Po ol -A (Mg C h a -1 ) k c k a R 2 Gle n Oro ua ST 44.40 0.81 0.00022 0.10 0.63 T an gi m oa na ST 42.40 0.97 0.00021 0.10 0.17 Kiw ite a ST 64.60 0.80 0.00032 0.10 0.39 Fei ld in g ST 35.80 0.45 0.00018 0.10 0.76 San so n ST 42.40 0.76 0.00021 0.10 0.63 T a b le 6 .3 : C oe ff ic ie nt o f de te rm in at io n (R 2 ) of t he d ai ly m ea su re d an d pr ed ic te d (m od el le d) C O 2-C f lu xes f or t he f ul l m ea su re m en t pe ri od o f th e fi ve s oi ls u se d in la bo ra to ry in cu ba tio n st ud y af te r ap pl ic at io n of th e tw o co m pa rt m en t m od el . So il si te s T ill ag e tr ea tm en t Po ol -C (Mg C h a -1 ) Po ol -A (Mg C h a -1 ) k c k a R 2 Gle n Oro ua (92 d ay s) ST 44.40 0.81 0.00022 0.10 0.83 T an gi m oa na (83 d ay s) ST 42.40 0.97 0.00021 0.10 0.13 Kiw ite a (81 d ay s) ST 64.60 0.80 0.00032 0.10 0.76 Fei ld in g (54 d ay s) ST 35.80 0.45 0.00018 0.10 0.71 San so n (99 d ay s) ST 42.40 0.76 0.00021 0.10 0.85 ST = Sim ul at ed ti lla ge 1 7 4 175 (a) 1 2 days (two compartment model) (b) 12 days (three compartment model) (c) 9 2 days (two compartment model) (d) 92 days (three compartment model) Figure.6.3: Comparison of observed and predicted (modelled) CO 2 -C fluxes by two and three compartment models for 12 days (a, b) and full 92 days (c, d) measurement period for the Glen Oroua site for the simulated tillage (ST) treatment. 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 2 4 6 8 10 12 M g CO 2- C ha -1 d -1 Days Predicted Observed R2=0.63 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 2 4 6 8 10 12 M g CO 2- C ha -1 d -1 Days Predicted Observed R2=0.82 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 10 20 30 40 50 60 70 80 90 100 M g CO 2- C ha -1 d -1 Days Predicted Observed R2=0.83 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 10 20 30 40 50 60 70 80 90 100 M g CO 2- C ha -1 d -1 Days Predicted Observed R2=0.95 176 (a) (b) Figure.6.4: Carbon dioxide contribution from pools-A & C of (a) two compartment model and pools-A, B and C of (b) three compartment models for the simulated tillage (ST) treatment for the Glen Oroua soil site. 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 10 20 30 40 50 60 70 80 90 100 M g CO 2- C ha -1 d -1 Days Pool-A Pool-C 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 10 20 30 40 50 60 70 80 90 100 M g CO 2- C ha -1 d -1 Days Pool-A Pool-B Pool-C 177 T a b le 6 .4 : T hr ee c ar bo n po ol s (Po ol -C , A a nd B), th ei r re sp ec tiv e de ca y co ns ta nt s (k c, k a an d k b) an d co ef fi ci en t of d et er m in at io n (R 2 ) of t he da ily m ea su re d an d pr ed ic te d (m od el le d) C O 2-C f lu xes f or 12 d ay s of th e fi ve s oi ls u se d in la bo ra to ry in cu ba tio n st ud y fo r de ve lo pi ng th e th re e co m pa rt m en t m od el . Soi l s ite s T ill ag e tr ea tm en t Po ol -C (Mg C h a -1 ) Po ol -A (Mg C h a -1 ) Po ol -B (Mg C h a -1 ) k c k a k b R 2 Gle n Oro ua ST 44.40 0.81 1.22 0.00022 0.10 0.0122 0.82 NT 44.40 0.81 1.22 0.00022 0.05 0.0122 0.46 T an gi m oa na ST 42.40 0.97 1.46 0.00021 0.10 0.0146 0.58 NT 42.40 0.97 1.46 0.00021 0.05 0.0146 0.44 Kiw ite a ST 64.60 0.80 1.21 0.00032 0.10 0.0121 0.61 NT 64.60 0.80 1.21 0.00032 0.05 0.0121 0.27 Fei ld in g ST 35.80 0.45 0.68 0.00018 0.10 0.0068 0.91 NT 35.80 0.45 0.68 0.00018 0.05 0.0068 0.44 San so n ST 42.40 0.76 1.14 0.00021 0.10 0.0114 0.76 NT 42.40 0.76 1.14 0.00021 0.05 0.0114 0.48 ST = Sim ul at ed ti lla ge NT = No -til la ge 177 178 T a b le 6 .5 : C oe ff ic ie nt o f de te rm in at io n (R 2 ) of t he d ai ly m ea su re d an d pr ed ic te d (m od el le d) C O 2-C f lu xes f or f ul l m ea su re m en t pe ri od o f th e fi ve s oi ls u se d in la bo ra to ry in cu ba tio n st ud y af te r ap pl ic at io n of th e th re e co m pa rt m en t m od el . So il si te s T ill ag e tr ea tm en t Po ol -C (Mg C h a -1 ) Po ol -A (Mg C h a -1 ) Po ol -B (Mg C h a -1 ) k c k a k b R 2 Gle n Oro ua (92 d ay s) ST 44.40 0.81 1.22 0.00022 0.10 0.0122 0.95 NT 44.40 0.81 1.22 0.00022 0.05 0.0122 0.89 T an gi m oa na (83 d ay s) ST 42.40 0.97 1.46 0.00021 0.10 0.0146 0.76 NT 42.40 0.97 1.46 0.00021 0.05 0.0146 0.88 Kiw ite a (81 d ay s) ST 64.60 0.80 1.21 0.00032 0.10 0.0121 0.84 NT 64.60 0.80 1.21 0.00032 0.05 0.0121 0.72 Fei ld in g (54 d ay s) ST 35.80 0.45 0.68 0.00018 0.10 0.0068 0.91 NT 35.80 0.45 0.68 0.00018 0.05 0.0068 0.76 San so n (99 d ay s) ST 42.40 0.76 1.14 0.00021 0.10 0.0114 0.92 NT 42.40 0.76 1.14 0.00021 0.05 0.0114 0.64 ST = Sim ul at ed ti lla ge NT = No -til la ge 1 7 8 179 6.3.3 Application of temporally dynamic three compartment model to field data After application of the three compartment model to predict CO 2 -C fluxes under laboratory conditions, the model was applied to predict CO 2 -C fluxes under field conditions. Carbon dioxide measurements were made during autumn and summer seasons from the Sanson field site. During the summer season simultaneous CO 2 -C measurements were made on in-situ soil cores in the laboratory along with field measurements. The model parameters derived from the summer laboratory incubation were used to predict summer season CO 2 -C fluxes. Laboratory CO 2 -C measurements were not conducted for the in-situ soil cores collected during the autumn season as the in-situ soil cores were placed in the field (Chapter- 4 section 4.3.2). Therefore the model was tested only for the summer field measurements. Under laboratory conditions of consta nt temperature and soil moisture, CO 2 -C fluxes measured over 4 hours were extrapolated to 24 hour daily CO 2 -C fluxes, therefore, the decay constants were set on 24 hour daily flux measurements. The three compartment model was restructured to predict the CO 2 evolved during the 4 hour measurement period i.e. the decay constants were adjusted according to the 4 ho ur measurement period rather than the 24 hour daily flux in the laboratory to model CO 2 -C fluxes in field. This change in model time step to 4 hour was made to accommodate a large diur nal change in temperature (section 6.3.4), when daily measurements were made at specific times and known temperatures and moistures. There were variations in soil temperature and moisture with rainfall in the field. Flux measurements were made between 9 am to 1pm in field and soil temperature and moisture were monitored on a regular basis. When temperature and moisture changes were not accounted for by the three compartment model, the coefficient of determination (R 2 ) showed a poor agreement between observed and predicted (modelled) CO 2 -C fluxes for both rotary tillage (RT) and No-tillage (NT) treatments (Figures 6.5 a, b) when a pplied to full length summer field measurements. In the field, CO 2 -C fluxes were affected by soil mo isture and temperature as large diurnal variations in soil temperature and moisture with rainfall were observed (Chapter-4, Figures 4.6b and 4.7b). This could explain the poor ag reement between observed and predicted (modelled) CO 2 -C fluxes for both rotary tillage (RT) and No-tillage (NT) treatments under field conditions. The observed C loss from RT and NT treatments for the 99 days measurement period in the field was 3.33 and 2.87 Mg C ha -1 and varied significantly between the tillage treatments. However, the pr edicted (modelled) C loss from RT and NT treatments for 99 days measurement period in the field was 2.50 and 2.49 Mg C ha -1 (Figures 6.6 a, b). Therefore, temperature and moisture scalars were added to the predictive model. 180 (a) (b) Figure.6.5: Comparison of observed and predicted (modelled) CO 2 -C fluxes by the three compartment model for 99 days measurement period for the Sanson site from (a) rotary tillage (RT) and (b) No-tillage (NT) treatments. (a) (b) Figure.6.6: Comparison of observed and predicted (modelled) cumulative CO 2 -C emissions by the three compartment model for 99 days measurement period for the Sanson site from (a) rotary tillage (RT) and (b) No-tillage (NT) treatments. 0.000 0.004 0.008 0.012 0.016 0.020 0 20 40 60 80 100 M g CO 2- C ha -1 4 hr -1 Days Predicted Observed R2=0.25 pool-C=42.40 (Mg C ha-1) pool-A=0.76(Mg C ha-1) pool-B=1.14(Mg C ha-1) ka=0.017 (4hr-1) kb=0.0019(4hr-1) kc=0.00004(4hr-1) 0.000 0.004 0.008 0.012 0.016 0.020 0 20 40 60 80 100 M g CO 2- C ha -1 4 hr -1 Days Predicted Observed R2=0.05 pool-C=42.40 (Mg C ha-1) pool-A=0.76 (Mg C ha-1) pool-B=1.14 (Mg C ha-1) ka=0.008 (4hr-1) kb=0.0019 (4hr-1) kc=0.00004 (4hr-1) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 20 40 60 80 100 M g CO 2- C ha -1 Days Predicted Observed 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 20 40 60 80 100 M g CO 2- C ha -1 Days Predicted Observed 181 6.3.4 Development of temporally and climatically dynamic three compartment model 6.3.4.1 Development of temperature scalar A range of literature based on field studies (Raich and Schlesinger 1992; Lloyd and Taylor 1994; Lenton and Huntingford 2003 ) and la boratory incubation studies at controlled temperature and soil moistures excluding roots (Kirschbaum 1995, 2000 ) was reviewed to determine an appropriate temperature response function to be used in the three compartment model so that predicted CO 2 fluxes from laboratory incubations could be made responsive to field temperature. Soil respiration measure ments under natural field conditions cannot provide an unbiased estimate of the temperature dependence of organic matter decomposition due to the confounding effects of soil moisture and seasonal substrate availability; moreover the contribution of root resp iration was not separated from that of soil microorganisms and fauna in field studies (Kirschbaum 2006). Therefore, it was decided to use the temperature response function obtained (Kirschbaum 2000) under highly controlled laboratory conditions to predict soil respiration under field conditions. The temperature dependence of decomposition of carbon pools following equation 6.4 was used in the three compartment model. The effect of temperature on decomposition rate of each carbon pool in equation 6.4 is mod elled by the response function of Kirschbaum (2000), normalised at a reference temperature of 30 oC as stated by Corbeels et al. (2005). rT? ??? ???????????????????? ???????.?????..(Eq. 6.4) Where: Ts ( oC) represents the soil temperature and 31.79 represents a constant derived by fitting the laboratory incubation data by Kirschbaum (2000). 182 Figure.6.7: Temperature scalar for the three compartment model showing relative change in CO 2 fluxes ( rT) normalised to account for laboratory CO 2 -C flux measurements being made at a constant temperature of 23 oC. The temperature response function ( rT) was interpolated over the range of soil temperatures (averaged over 4 hours) measured in the field during which 4 hour CO 2 -C flux measurements were made (Figure 6.7). The decay constants (Table 6.5) used in the three compartment model accurately described CO 2 evolution in the laboratory at 23 oC constant temperature. Therefore, these rT values need normalising about 23 oC. A temperature scaling factor (T sl) was derived by equating the rT values calculated from equation 6.4 for 23 oC to 1 and is presented as secondary axis in Figu re 6.7. The equation 6.4 used for calculating rT values does not take into account the soil temperature and moisture interaction. 6.3.4.2 Development of moisture scalar Soil moisture is an important abiotic driver of soil C dynamics (O? Brien et al. 2010). Importance of moisture content has been demonstrated in temperature controlled laboratory studies where roots are excluded (Davidson et al. 1998). Variations in soil moisture affect the diffusion of soluble substrates at lower moisture content (Kechavarzi et al. 2010) whereas high moisture content limits the diffusion of oxygen (Hashimoto and Komatsu 2006; Skopp et al. 199 0; Linn and Doran 198 4). Owing to confounding effects of soil 183 moisture and temperature on soil respiration, application of laboratory based understanding of the effects of soil moisture on soil respiration to field studies has not been fully achieved (Reichstein et al. 2005; Davidson et al. 2000, 1998). Several empirical relationships have been established between field soil respiration and soil moisture and temperature (Almagro et al. 2009; Yuste et al. 2007; Xu et al. 2004). However, these relationships were site specific hence no appropriate soil moisture function was found from the literature. Therefore, the field data was scrutinised and a period where soil temperature was approximately constant but moisture varied was selected. The first step was to find a relationship between CO 2 -C fluxes and moisture content. The CO 2 -C flux values measured from rotary and No- tillage treatments during that period were plotted against the observed gravimetric moisture content (GMC) of soil in the field and poly nomial regression equations were fitted to the data (Figures 6.8 a, b): y = -2E-05x 2 + 0.0011x - 0.0074 (Figure 6.8 a) y = -5E-07x 2 + 0.0003x - 0.0014 (Figure 6.8 b) The second step was to normalise the data to produce a moisture scalar. The decay constants used in the three compartment model accurately described CO 2 evolution in the laboratory at constant ~15% GMC for Sanson site soil. Therefore, the predicted CO 2 -C flux values derived from these polynomial equations were normalised by equating for 15.0% GMC to 1 and is present as secondary axis in Figures 6.8 a, b. The relationship for the scalar values for different moisture contents are given in the equation 6.5 and 6.6 and also shown in Figures 6.8 a, b. Moisture factor (w s) = -0.0043x 2 + 0.2391x - 1.6087 (Figure 6.8 a) ?????.?.. (Eq. 6.5) Moisture factor (w s) = -0.0002x 2 + 0.1004x - 0.4686 (Figure 6.8 b) ????.??.. (Eq. 6.6) A small decrease in the soil CO 2 -C flux values (Figure. 6.8 a, b) may be attributed to the development of anaerobic conditions in the soil which would have decreased the aerobic microbial activity. Linn and Doran (1984 ) also ob served a decrease in the aerobic microbial activity with increasing soil moisture content. These temperature and moisture functions were constrained within the observed (measured) field soil temperature and moisture values. 184 (a) (b) Figure.6.8: Moisture scalars shown by dotted lines for the three compartment model derived from field observations of CO 2 -C fluxes at a range of soil moisture contents in (a) rotary tillage (RT) and (b) No-tillage (NT) treatme nts. Solid lines represent the relationship between the measured CO 2 fluxes and GMC?s of soil in the field. y = -2E-05x2 + 0.0011x - 0.0074 Ws= -0.0043x2 + 0.2391x - 1.6087 0.0 0.5 1.0 1.5 2.0 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0 5 10 15 20 25 30 35 40 45 Sc al ar M ea su re d CO 2 f lu xe s (M g CO 2- C ha -1 4 hr -1 ) GMC (%) y = -5E-07x2 + 0.0003x - 0.0014 Ws = -0.0002x2 + 0.1004x - 0.4686 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0 5 10 15 20 25 30 35 40 45 Sc al ar M ea su re d CO 2 f lu xe s (M g CO 2- C ha -1 4 hr -1 ) GMC (%) 185 The three compartment model including moistu re and temperature factors to describe the flux rates of CO2 from tilled soil: Fn = (T sl x ws x ((C n x kc) + (A n x ka) + (B n x kb))) ????????..????.. (Eq. 6.7) Where: F n= Flux rate for 4 hour period n (Mg C ha -1 4hr -1 ) Stable-C pool (Mg C ha -1 ) is represented as C (pool-C), initial stable-C pool value was determined by multiplying total-C concentratio n by bulk density and its subsequent values i.e. Cn for 4 hour period n were determined using the following formula: Cn= [C (n-1) ?(T sl x w s x (C (n-1) x k c))] The most labile or active-C pool (Mg C ha -1 ) is represented as A (pool-A), initial size was set to be the amount of C lost as CO 2 during 12 days incubation from disturbed/simulated tilled soils under controlled laboratory conditions and its subsequent values i.e. A n for 4 hour period n were determined using the following formula: An= [A (n-1) ? (T sl x w s x (A (n-1) x k a))] Intermediate-C pool (Mg C ha -1 ) is represented as B (pool-B); initial size was set as; Pool-B (Initial) = Pool-A (Initial) x 1.5 and its subsequent values i.e. B n for 4 hour period n were determined using the following formula: B n= [B (n-1) ? (T sl x w s x (B (n-1) x k b))] Tsl= Temperature factor ??????????????????..?????. (Eq. 6.4) ws= Moisture factor ????????????????????.??..??... (Eq. 6.5) The decay constants k c, k a and k b used in equation 6.7 were the same as described in equation 6.2 with adjustment to the 4 hour measurement period. The three compartment model including moisture and temperature factors to describe the flux rates of CO2 from No-tilled soil: Fn = (T sl x ws x ((C n x kc) + (A n x ka/2) + (B n x kb))) ???????......???.. (Eq. 6.8) The model parameters described in equation 6.7 for the tillage treatment were used to predict CO 2 -C fluxes from No-tillage treatment only by changing the value of decay constant (k a) of pool-A and moisture factor. Th e value of decay constant (k a) of pool-A to predict the CO 2 -C fluxes from No-tillage was half the value used to fit the CO 2 -C fluxes measured from the tillage treatment. Tsl= Temperature factor ????????????????????.???.. (Eq. 6.4) ws= Moisture factor ?????????????????????..??.?... (Eq. 6.6) 186 6.3.4.3 Application of temporally and climatically dynamic three compartment model to field data The three compartment model including temperature and moisture factors, when applied to the full length summer field measurements showed improvement in the coefficient of determination (R 2 ) between observed and predicted (modelled) CO 2 -C flux values for both rotary tillage (RT) and No-tillage (NT) treatment s (Figures 6.9 a, b) in comparison to when the model was applied without these factors (Figures 6.5 a, b). The observed C loss from RT and NT treatments during the 99 days measurement period in field was 3.33 and 2.87 Mg C ha -1 , and varied significantly between the tillage treatments. However, the predicted (modelled) C loss from RT and NT treatments was 2.30 and 2.80 Mg C ha -1 (Figures 6.10 a, b). The model accura tely predicted the amount of C lost from NT treatment but the predicted amount for the RT treatment was 30 per cent less than the amount that was lost from the RT treatment in field. Moreover, the predicted amounts of C lost were higher for NT than RT treatmen t which was contrary to the findings from the field study. For the RT treatment, the model underestimated the higher peaks of CO 2 -C fluxes observed after rainfall events thus ex plaining why the predicted C loss for the RT treatment was less than the observed C loss. High CO 2 -C fluxes after rainfall events were thought to be the result of increased mineralization of the lysed microbial cells caused by desiccation or due to rapid rewetting (Magid et al. 1999; Fierer and Schimel 2003) and moisture stimulated microbial activity (Kessavalou et al. 1998). However, the model predicted the CO 2 -C fluxes well in the NT treatment. Since moisture content remained higher in the NT than RT treatment throughout the measurement period (Chapter-4, Figure 4.7b), wetting and drying may not have caused severe microbial lyses and enhanced microbial activity and CO 2 -C loss in the NT treatment. More over the moisture scalar in the model predicted higher CO 2 -C fluxes due to higher moisture co ntents in the NT treatment. Thus the model is unable to predict the CO 2 -C loss from the RT soil. The current three compartment model selects the carbon pools on a chemical analysis basis and do not take the dynamic nat ure of microbial biomass into account. Further research work is required if the three compartmen t decomposition model is to be improved to simulate short term C loss from NT and RT soil s within the season of tillage. It may be advantageous to estimate the crop or pasture residue carbon and use that to determine rapidly decomposing pool sizes. In addition to accommodating wetting and drying it will be necessary to incorporate a microbial biomass pool in the model which can increase as residue carbon is decomposed but decrease when soil moisture drops below wilting point and when 187 the soil subsequently wets up and becomes a po ol of rapidly decomposing C. Therefore in future studies, an improvement in model devel opment would arise if laboratory incubations are conducted at a range of soil temperatures and moistures and microbial biomass measurements are made on a frequent basis to quantify the microbial biomass C pool. This will produce independent scalars for temperature and moisture that could be validated on the field data. The data obtained during this Ph.D. study wa s not sufficient to provide or develop a model that could be used to predict CO 2 loss from conventional and No-tillage cultivation in New Zealand soils based on a 12-day laboratory incubation. 188 (a) (b) Figure.6.9: Comparison of observed and predicted (modelled) CO 2 -C fluxes by the three compartment model including temperature and moisture factors for the Sanson site from (a) rotary tillage (RT) and (b) No-tillage (NT) treatments for 99 days measurement period. (a) (b) Figure.6.10: Comparison of observed and predicted (modelled) cumulative CO 2 -C emissions by the three compartment model including temperature and moisture factors for the Sanson site from (a) rotary tillage (RT) and (b) No-tillage (NT) treatments for 99 days measurement period. 0.000 0.004 0.008 0.012 0.016 0.020 0 20 40 60 80 100 M g CO 2- C ha -1 4 hr -1 Days Predicted Observed R2=0.30 pool-C=42.40 (Mg C ha-1) pool-A=0.76(Mg C ha-1) pool-B=1.14(Mg C ha-1) ka=0.017 (4hr-1) kb=0.0019(4hr-1) kc=0.00004(4hr-1) 0.000 0.004 0.008 0.012 0.016 0.020 0 20 40 60 80 100 M g CO 2- C ha -1 4 hr -1 Days Predicted Observed R2=0.47 pool-C=42.40 (Mg C ha-1) pool-A=0.76 (Mg C ha-1) pool-B=1.14 (Mg C ha-1) ka=0.008 (4hr-1) kb=0.0019 (4hr-1) kc=0.00004 (4hr-1) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 20 40 60 80 100 M g CO 2- C ha -1 Days Predicted Observed 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 20 40 60 80 100 M g CO 2- C ha -1 Days Predicted Observed 189 6.4 Summary and conclusions The concept was to use the amount of CO 2 emitted from the 12 days laboratory incubation to predict the field soil C loss due to conventional and No-tillage practices. Firstly a simple regression model was used and then complex tw o and three compartment models were used to predict the soil C loss. The coefficient of determination (R 2 ) using regression model showed a strong relationship between CO 2 -C emitted in 12 days and full laboratory incubation period for both simulated tillage (ST) and No-tillage (NT) treatments but a poor relationship was observed for CO 2 -C emitted during the full measurement period in the field for rotary (RT) and No-tillage (NT) treatment, suggesting the need for complex two and three compartment models to simulate CO 2 -C emissions in field varying in soil temperature and moisture. The two compartment model comprising active and stable-C pools, developed from 12 days laboratory incubation data of ST trea tment when applied to predict full incubation laboratory fluxes for ST treatment underestimated the CO 2 -C fluxes after few days. Therefore, to increase the simulated fluxes, an intermediate C pool was included in the two compartment model resulting in the three compartment model. The decay constant of the active C pool in the three compartment model was adjusted to predict CO 2 -C fluxes for the No-tillage treatment. The coefficient of determination (R 2 ) using the three compartment model showed a good agreement between predicted (modelled) and observed CO 2 -C fluxes from ST and NT treatments under laboratory conditions but a poor relationship was found for CO 2 -C fluxes measured in field from RT and NT treatments. To make the laboratory fluxes align to field fluxes, temperature and moisture functions were included in the three compartment model. The three compartment model including temperature and moisture functions when applied to predict CO 2 -C fluxes in field improved the coefficient of determination (R 2 ) values for both RT and NT treatments in comparison to when the three compartment model without temperature and moisture functions was applied. The model accurately predicted the amount of C lost from the NT treatment; however, the predicted amount for th e RT treatment was 30 per cent less than the amount that was lost from the RT treatment in field. The predicted amounts of C lost were higher for NT than RT treatment which was in contrast to the findings from the field study. The high peaks of CO 2 -C fluxes observed after rainfall events for RT treatment were underestimated by the applied model which resulted in lower predicted C loss than the 190 observed C loss. The data obtained during this Ph.D. study was not sufficient to provide or develop a model to predict CO 2 loss from conventional and No-tillage cultivation in New Zealand soils. 191 6.5 References Almagro, M., Lopez, J., Querejeta, J. I., & Martinez-Mena, M. (2009). Temperature dependence of soil CO 2 efflux is strongly modulated by seasonal patterns of moisture availability in a Mediterranean ecosystem. Soil Biology & Biochemistry, 41 ( 3 ), 594- 605. Bayer, C., Lovato, T., Dieckow, J., Zanatta, J. A., & Mielniczuk, J. ( 2006). A method for estimating coefficients of soil organic matter dynamics based on long-term experiments. Soil & Tillage Research, 91 (1-2), 21 7-226. Bruun, S., Christensen, B. T., Hansen, E. M., Magid, J., & Jensen, L. S. (2003). Calibration and validation of the soil organic matter dynamics of the Daisy model with data from the Askov long-term experiments. Soil Biology & Biochemistry, 35 (1), 67 - 76. Corbeels, M., McMurtrie, R. E., Pepper, D. 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A simp le chemical method of assessing potentially available organic nitrogen in soil. Communications in Soil Science and Plant Analysis, 17 (2), 195-214. Hashimoto, S., & Komatsu, H. (2006 ). Relationships between soil CO 2 concentration and CO 2 production, temperature, water content, an d gas diffusivity: implications for field studies through sensitivity analyses. Journal of Forest Research, 11 (1 ), 41-50. Jenkinson, D. S. (1990). The turnover of or ganic-carbon and nitrogen in soil. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences, 329(1255), 361-368. 192 Kechavarzi, C., Dawson, Q., Bartlett, M., & Leeds-Harrison, P. B. (2010 ). The role of soil moisture, temperature and nutrient amendment on CO 2 efflux from agricultural peat soil microcosms. Geoderma, 154 (3-4), 203-210. Kessavalou, A., Doran, J. W., Mosier, A. R., & Drijber, R. A. (1998). Greenhouse gas fluxes following tillage and wetting in a wheat-fallow cropping system. Journal of Environmental Quality, 27 (5), 1105-1116. Kirschbaum, M. U. F. (1995). The temperature-dependence of soil organic-matter decomposition, and the effect of glob al warming on soil organic-C storage. Soil Biology & Biochemistry, 27 (6), 753-760. Kirschbaum, M. U. F. (2000). Will changes in soil organic carbon act as a positive or negative feedback on global warming? Biogeochemistry, 48 (1), 21-51. Kirschbaum, M. U. F. (2006). The temperature dependence of organic-matter decomposition - still a topic of debate. Soil Biology & Biochemistry, 38 (9), 2510-2518. La Scala Jr, N., Lopes, A., Spokas, K., Arche r, D. W., & Reicosky, D. C. (2009). Short-term temporal changes of bare soil CO 2 fluxes after tillage described by first-order decay models. European Journal of Soil Science, 60 (2), 258-264. La Scala, N., Bolonhezi, D., & Pereir a, G. T. (2006). Short-term soil CO 2 emission after conventional and reduced tillage of a no-t ill sugar cane area in southern Brazil. 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Effect of water-filled pore-space on carbon dioxide and nitrous oxide production in tilled and non-tilled soils. Soil Science Society of America Journal, 48 (6), 1267-127 2. Lloyd, J., & Taylor, J. A. (1994). On the temper ature-dependence of soil respiration. Functional Ecology, 8(3 ) , 315-323. Magid, J., Kjaergaard, C., Gorissen, A., & Kuik man, P. J. (1999). Drying and rewetting of a loamy sand soil did not increase the turnover of native organic matter, but retarded the decomposition of added C-14-labelled plant material. Soil Biology & Biochemistry, 31 (4), 595 -602. McLauchlan, K. K., & Hobbie, S. E. (2004 ). Comparison of labile soil organic matter fractionation techniques. Soil Science Society of America Journal, 68 (5), 1616-1625. O'Brien, S. L., Jastrow, J. D., Grimley, D. A., & Gonzalez-Meler, M. A. (2010). Moisture and vegetation controls on decadal-scale accrual of soil organic carbon and total nitrogen in restored grasslands. Global Change Biology, 16 (9), 2573-2588. Parton, W. J., Schimel, D. S., Cole, C. V., & Ojima, D. S. (1987). Analysis of factors controlling soil organic-matter levels in great-plains grasslands. Soil Science Society of America Journal, 51 (5 ), 1173-1179. Raich, J. W., & Schlesinger, W. H. (1992). The gl obal carbon-dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus Series B-Chemical and Physical Meteorology, 44(2), 81 -9 9. Reichstein, M., Subke, J. A., Angeli, A. C ., & Tenhunen, J. D. (2005). Does the temperature sensitivity of decomposition of soil organic matter depend upon water content, soil horizon, or incubation time? Global Change Biology, 11 (10), 1754-1767. Reicosky, D. C., & Lindstrom, M. J. (1993). Fall tillage method - effect on short-term carbon-dioxide flux from soil. Agronomy Journal, 85 (6), 1237-1243. Rochette, P., & Angers, D. A. (1999). Soil surf ace carbon dioxide fluxes induced by spring, summer, and fall moldboard plowing in a sandy loam. Soil Science Society of America Journal, 63 ( 3 ) , 621-628. Six, J., Elliott, E. T., Paustian, K., & Do ran, J. W. (1998). Aggregation and soil organic matter accumulation in cultivated and native grassland soils. Soil Science Society of America Journal, 62 (5 ), 1367-1377. Smith, P., Smith, J. U., Powlson, D. S., McGill, W. B., Arah, J. R. M., Chertov, O. G., et al. (1997). A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma, 81 (1-2), 153 -225. 194 Skopp, J., Jawson, M. D., & Doran, J. W. (1990). Steady-state aerobic microbial activity as a function of soil-water content. Soil Science Society of America Journal, 54 ( 6 ), 1619- 1625. van Groenigen, K. J., Hastings, A., Forristal, D., Roth, B., Jones, M., & Smith, P. (2011). Soil C storage as affected by tillage and straw management: An assessment using field measurements and model predictions. Agriculture Ecosystems & Environment, 140(1-2), 218-225. Wagner, S. W., Reicosky, D. C., & Alessi, R. S. (1997). Regression models for calculating gas fluxes measured with a closed chamber. Agronomy Journal, 89 ( 2 ), 279 - 284. Wildung, R. E., Garland, T. R., & Buschbom, R. L. (1975 ). Interdependent effects of soil temperature and water-content on soil respiration rate and plant root decomposition in arid grassland soils. Soil Biology & Biochemistry, 7(6), 373-378. Xu, L. K., Baldocchi, D. D., & Tang, J. W. (2004). How soil moisture, rain pulses, and growth alter the response of ecosystem respiration to temperature. Global Biogeochemical Cycles, 18(4). Yuste, J. C., Baldocchi, D. D., Gershenson, A., Goldstein, A., Misson, L., & Wong, S. (2007). Microbial soil respiration and its dependency on carbon inputs, soil temperature and moisture. Global Change Biology, 13 (9), 2018-2035. 195 Chapter-7 Conclusions and recommendations for future work 7.1 Conclusions Review of the literature suggested that No-tillage seeding conserves soil C by reducing CO 2 losses compared with conventional tillage practices. Quantitative information on the reduction of CO 2 emissions with No-tillage practic e in comparison to conventional tillage is limited in New Zealand. Moreover, th e effects of soil and climatic conditions and amounts of plant residue at cultivation on these emissions from cropping/pasture systems under a temperate climate, such as for New Zealand, are not well documented, nor are they well quantified. Therefore, information on the reductions of CO 2 emissions with Cross Slot ? No-tillage cultivation in comparison to the c onventional cultivation operations practised in New Zealand and a process-based understanding of the soil and climatic factors controlling CO 2 losses is required. A series of laborator y and field experiments were conducted to test the hypothesis that Cross Slot ? No-tillage cultivation is a management strategy for conserving and perhaps increasing soil C in cropping and pasture soils. 7.1.1 Selection of appropriate method for measuring CO2 fluxes The objective was to establish a suitable method for measuring CO 2 emissions from seedbeds. Carbon dioxide fluxes are mea sured by a number of techniques i.e. micrometeorological and static and dynamic chambers, with large differences in accuracy, spatial and temporal variability, and applicability. To date, ch ambers are the most commonly used for measuring the emissions of greenhouse gases. To establish a suitable chamber method for collecting and analysing CO 2 , three different approaches were investigated. Static alkali traps, flux gradient and a portable infra-red gas analyser (IRGA), were compared under laboratory conditions using low (sub-surface) and high (surface) CO 2 -emitting soil (Chapter-3). Based on the results of this study, the alkali trap method was selected as the most appropriate for collecting and measuring CO 2 fluxes from cultivated or No-tilled agricultural soils. Flux measurements made by EGM-1 were highly variable; moreover, this method requires numerous replicated spot measurements. The flux gradient method was sensitive to large increases in the concentration of emitted CO 2 and is expensive compared to the alkali trap. To determine whether the impact of pressure fluctuation caused by wind velocity was a significant factor with different effects on soil CO 2 emissions from tilled or no-tilled soils, 196 a two week laboratory study was conducted using in-situ soil cores. Two tillage treatments, simulated-tillage (ST) and Cross Slot ? No-tillage (NT), and the third treatment was a non- disturbed (ND) control. Negati ve pressure of 20 millibars was maintained in each replicate chamber by using an individual bubble tower. Results of the laboratory study corroborate the findings in the literature that higher CO 2 fluxes are caused by the onset of lower (negative) air pressure. The effect of pressure change on soil CO 2 fluxes differed with the amount of soil disturbance. For simulated tilled soil (ST) the effect was short-lived (4 hours) and only occurred in the first week. However, for NT and ND soils, the difference between the rate of CO 2 loss between ambient pressure and the onset of lower pressure remained for the two week period. Resulting from this study it was decided that for further studies CO 2 -C measurements should be conducted at ambient pressure conditions. 7.1.2 Reduction in CO2 emissions with No-tillage To quantify the potential savings of soil C with Cross Slot ? No-tillage (NT) cultivation, CO 2 fluxes were measured in the laboratory for up to 3-months from in-situ soil cores collected from four different sites varying in physico- chemical properties collected across Manawatu region during the summer season (December 2009 and January 2010 ) (Chapter-4). The total amount of CO 2 emitted under laboratory conditions from the four soils ranged between 1086 and 3264 kg CO 2 -C ha -1 for simulated tillage (ST), 973 and 2871 kg CO 2 -C ha -1 for No-tillage (NT) and 953 and 3023 kg CO 2 -C ha -1 for non-disturbed (ND) treatment. In general, three out of the four soils lost more CO 2 from ST (between 113 and 393 kg CO 2 -C ha -1 ) than they did for the NT treatment , although the differences were not significant at a 5% level of significance. In order to verify that differences in CO 2 fluxes observed between ST, NT and ND treatments from laboratory situations represented the magnitude of field emissions; CO 2 measurements were also made for one of the soils at a field site near Sanson during autumn (/winter) (April to July 2010) and (spring/) summer (November 2010 to February 2011 ) season. The three tillage treatments compared in the field trial were: 1) tillage with rotary tiller (RT) to the depth of 10 cm followed by bar harrow, 2) direct seeding with Cross Slot ? No-tillage drill (NT), and 3) a non-disturbed (ND ) control. The control treatment had no soil disturbance by tillage. Static chambers were installed immediately over the slots after the Cross Slot ? No-tillage seeding. In conjunction with field measurements, CO 2 measurements were made from in-situ soil cores. Closed-base static chambe rs enclosing the soil cores were placed in the field during the autumn season and in the laboratory during the summer season. 197 Measurements continued for 110 days during the autumn season and 99 days for the summer season. Under field conditions, total CO 2 emissions were significantly higher from RT (2580 kg CO 2 -C ha -1 ) than NT (2215 kg CO 2 -C ha -1 ) and ND (2405 kg CO 2 -C ha -1 ) treatments during the autumn season, and from RT (3330 kg CO 2 -C ha -1 ) than NT (2877 kg CO 2 -C ha -1 ) and ND (2807 kg CO 2 -C ha -1 ) treatments during the summer season at a 5% level of significance. Overall, the results of the field study suggest that Cross Slot ? NT cultivation resulted in a combined annual conservation of ~3.0 Mg CO 2 ha -1 (818 kg CO 2 -C ha -1 ) in comparison to the RT treatment from bare soils. Carbon dioxide measurements made from both the field chambers and in-situ soil cores placed in the field following RT/ST, NT and ND treatments were simila r in magnitude (~2.5 Mg CO 2 -C ha -1 ) during the autumn season. However, during the summer season the magnitude of CO 2 emissions from RT/ST, NT and ND treatments both in the field and laborat ory were not similar. Under field conditions, carbon dioxide emissions were ~3.0 Mg CO 2 -C ha -1 compared with ~ 2.0 Mg CO 2 -C ha -1 for laboratory-simulated conditions during the summer season. Lack of variation in soil moisture and temperature in laboratory incubated soil cores resulted in the difference in magnitude of CO 2 emissions from field and soil cores. Therefore, the concept that soil cores from the field being brought into the laboratory and tillage-tr eatment simulated to get reliable estimates of CO 2 emissions under field conditions appears to be unworkable if field soil climate conditions are not simulated. 7.1.3 Influence of soil type and residue input rates on CO2 emissions The soils used in the laboratory and field experiments quantifying CO 2 emissions differed in residue amounts, texture (sandy loam and silt loam) and moisture status. Therefore a laboratory study was conducted to assess the influence of soil type on the decomposition of pasture residues (Chapter-5 ). Four soils: Carnarvon sandy loam, Ohakea stony silt loam, Ohakea silt loam (used in the laboratory and field study) and an allophanic, Horotiu silt loam received ryegrass pasture residues at 0, 5.11, 6.81, 8.51 mg C g -1 oven- dried soil equivalent to 0, 12, 16 and 20 Mg oven-dried residues ha -1 and were incubated in Agee jars at 80% of their field capacity moisture content for 129 days. During the 129 days incubation, the proportion of pasture residue decomposed did not differ significantly between differences in the soil texture and the residue input rates. In order to develop a potential soil test to predict CO 2 emissions, different labile-C fractions (HWC, MBC, H 2 O 2 -C, K 2 Cr2 O 7 -C and K 2 SO 4 -C) were determined on day zero i.e. before starting the laboratory decomposition study from all the four soils. All the labile-C 198 fractions were correlated with the total amount of CO 2 respired during the incubation period. However, none of the labile-C fractions showed strong relationships with the CO 2 respired during the incubation period. Therefore none of these laboratory soil tests can be used to predict the CO 2 losses. Hence, a combination of laborat ory incubation and a predictive model to predict the CO 2 losses from conventional and No-tillage soils was explored. 7.1.4 Modelling laboratory CO2 fluxes to predict field CO2 emissions In an effort to develop a CO 2 predictive model, relationships between CO 2 respired in the short term and the total amount lost during the entire measurement period following cultivation were explored (Chapter-6). Based on the CO 2 measurements done on in-situ soil cores under laboratory conditions and collected from different sites (Chapter-4), the amount of CO 2 respired during 12 days incubation showed a highly significant relationship with the total amount respired during the entire measurement period. Hence, 12-day soil incubation was selec ted to develop a predictive model, as the commercial test cannot be longer as it has to provide timely advice to the farmer. Based on 12 day laboratory incubation data, a simple static regression model was developed. However, a weak relationship between laboratory and fiel d data suggested that a more complex carbon decomposition compartment model was require d. Therefore, two and three compartment models were developed and tested to predict full-term laboratory incubations. When tested to predict the amount of CO 2 evolved during the full-term laboratory incubation, the three compartment model gave a higher coefficient of determination in comparison with the two compartment model due to the inclusion of the third intermediate-C pool. To predict the field CO 2 emissions, temperature and moisture factors were included in the three compartment model because laboratory incubations were carried out at constant temperature and moisture but in the field both these factors are variable. The temperature and moisture sensitive model was used to predict the CO 2 emissions measured during the summer season. The model accurately predicted the amount of C lost from No-tillage soils (observed= 2.87 Mg C ha -1 ; predicted (modelled) = 2.80 Mg C ha -1 ) but the predicted amount for rotary tilled soils (predicted (modelled) = 2.30 Mg C ha -1 ) was 30 per cent less than the amount that was lost in the field (observed= 3.33 Mg C ha -1 ). The model predicted C loss was higher for No-tillage soils than the rotary tilled soils which was contradictory to what was observed in the field. Therefore further work is required as the data obtained during this Ph.D. study was insufficient to provide or develop a model that could be used to predict CO 2 loss from 199 conventional and No-tilla ge cultivation in New Zealand soils based on 12-day laboratory incubations. 7.2 Contribution to New Zealand agriculture New Zealand?s total greenhouse gas emissions are minor from a global perspective (0.5% of global emissions) ( http://www.landcareresearch.co.nz/science/greenhouse-gases ). New Zealand?s economy is largely agriculture -based therefore New Zealand?s greenhouse gas emissions profile is unique in comparison to other developed countries as agriculture is the main contributor (35.3 Mt-CO 2 -eq) of the total greenhouse gas emissions (77.0 Mt-CO 2 - eq) (projections by year for 2012) ( http://www.mfe.govt.nz/publ ications/climate/greenhouse- gas-inventory-2012 ). Results of the field trial conducted during this Ph.D. study suggest that Cross Slot ? NT cultivation resulted in a combined annual conservation of ~3.0 Mg CO 2 -eq ha-1 (818 kg CO 2 -C ha -1 ) in comparison to rotary till age cultivation. The carbon dioxide released from approximately 800,000 hectares whic h continue to be tilled annually with conventional tillage practices in New Zealan d (Personal Communication C J Baker 2009, see Appendix 1) could be about 2.4 Mt-CO 2 -eq per year (800,000 X 3 Mg CO 2 -eq ha -1 ). Conversion of all 800,00 0 hectares which continue to be tilled annually in New Zealand to Cross Slot ? NT seeding could reduce the contribution of agriculture to the total New Zealand greenhouse gas emissions by 7.0% per year. 7.3 Future research The research work described in this thesis suggests some areas that require further investigation. x The laboratory and field CO 2 measurements in this study were made on the sites that were established under Cross Slot ? No-tillage cultivation during the last 2 to 15 years owing to unavailability of the plots paired with other forms of tillage. Long-term field sites are needed to examine the effect of No -tillage seeding on soil carbon dynamics and CO 2 conservation in comparison to conventional tillage practices. Paired plots comparing the two tillage systems should be established at different sites varying in soil and climatic factors in New Zealand to provide precise sc ientific information regarding the benefits of No-tillage to farmers. x The decomposition study carried out in this Ph.D. study used four soils with a narrow range of textures (sandy loam & silt loam) and used only ryegrass pasture residues. Further studies using crop residues (barley and maize) and a wider range of soil 200 types/texture (sand & clay) c ould be helpful in understanding the decomposition pattern of different crop residues in different soils types in New Zealand. x The model developed on the basis of 12 days ? laboratory incubation data could not predict the CO 2 emissions from rotary tilled soils in the field accurately. Further studies are required to examine the effect of drying-rewetting events on soil CO 2 fluxes which could be helpful in predicting the CO 2 fluxes accurately in the event of rainfall, and these studies need to be done over a range of temperatures and soil moistures to account for the microbial biomass C pool. 201 Ap p e n d i x 1 Q U ANT I F Y I N G T HE HECT AR E S OF L AND U NDE R NO -T IL L AG E IN NEW ZE AL AN D ( a ) Fore st r y T her e wer e 1. 7 m illio n hec t ares of plan ted fores ts in NZ in 1999 (MAF st atis t ic s , 1999) . (b) Ar abl e crops T here wer e 21 2,0 00 hectar es of arable crops gro wn in Ne w Ze aland in 199 9 (M A F statis tic s 19 99). T he areas of annu al arab le croppin g in NZ var y wi de l y ye ar -b y- ye ar a nd ar e large l y dri ve n b y pr ic e fluctuations . T he 19 99 fig ure was in fact lo wer th an the 10 - ye ar a vera ge bec aus e it ref lected relat iv e l y lo w crop r eturn s at the tim e. W ith arable crops , th e soil r equ ires till age bot h bef ore and af ter the crop (t he latt e r in ord er to establ is h the next crop or r eturn the lan d to pas ture). T he total area tille d an nu all y in rel ation to ara ble crops is theref ore 2 X 212, 000 = 424, 000 hec tares . ( c ) Pastu re Ther e wer e 10 .3 m illi on he c tares under pas ture in NZ in 1999 (MAF st atis t ic s 199 9). Ap prox im at el y 45% (4. 6 m ha) is bel ie ve d to be acc ess ible to tracto r s . T ract or - a c c e ss ib le pastur e s are rene we d at leas t ever y 20 year s . T her e is a gro wi ng tre nd to wards rene wing pas tures m ore frequen t l y but a cons ervat i ve 20- ye ar c yc l e is used in thes e com paris o ns. Theref ore approx im atel y 23 0,00 0 ha (4,6 00,0 00 X 0. 05 ) of new lo ng-term pas tures are so wn an nua ll y in Ne w Ze ala nd. To avoid cou nting the ar ea sown to pas ture twic e (s ince m uch of this area is com m on with the pos t - c r op area that is till ed af ter arabl e or forage crop s ) we hav e ass um ed that all of th e arab le crops wil l be retur ned to pasture. In practice, such is not th e cas e. Som e is returned to fora ge crops an d other arable crops an d som e of these crops , in tur n, are re tu rned to pas tur e. But the si m plif ied ass um ption abo ve allo ws th e ca lc ula t io ns to be m ore trans p aren t and has no ef f ect on the fin al figure or conc lus io ns . Ass um ing that all of the sec ond crop af ter an arab le crop is so wn to lo ng -te r m pas ture , th e com bine d annu al area tille d for arab le crops and ne w lo ng -t erm pas tures per year in NZ is approxim atel y 406, 000 ha {4 24, 000 ?( 23 0 ,000 - 2 12, 000)} . ( d ) Forag e crop s It is estim ated that appr oxi m atel y 24 0,0 00 ha are s o wn to bras s ic a forage crops annu all y in NZ (seed ind ustr y est im ate, 200 2). It is be lie ve d th at abo ut 35% of all forage crops in NZ ar e no n - bras s ic a spec ies such as annu a l r ye gr a s s e s , chic o r y, sp ecia ll y - s own sil age crops (rather t han si la ge harvest ed from surplus lon g -term pas tures ) and forag e oats (to tal, 84 ,00 0 ha). The total area so wn ann ual l y to forage cro ps in NZ is theref ore ap prox im atel y 32 4 ,000 ha (2 40, 000 + 84,0 00). As wit h ara ble crops, two til lag e even ts tak e plac e for ever y on e forage crop gro wn (one bef ore an d one af ter the crop). The total area tille d for f orage crops an nu all y in NZ is theref ore ap prox im atel y 64 8 ,000 ha. 202 T HE T OT AL AR E A T ILL ED ANNU ALLY FO R AGRI CULT UR AL CRO PS IN N E W ZE AL AN D (T H AT H AS T HE POT ENT IAL T O BE CON VE RT ED TO N0 -T ILL AG E) IS AP PR OXI M AT E L Y 954, 000 ( 648, 000 +4 06, 000 ; S AY 1 M ILL ION) HECT ARES HOW M UCH NO -T ILL AG E IS CU RRE NT LY PR ACT I SED IN NZ? It is belie ved that ap prox im atel y 200, 000 ha of crop an d pasture esta blis hm ent in Ne w Zea la nd is undert ak en using no -t il lag e m ethod s (indust r y estim at e ) . T his repr esents 20% of annu al cro p an d pas ture est ab lis hm ent. Approx im atel y 800, 000 hec tares theref or e co ntinue to be ti l led ann ua ll y. CONCLUS I O N S ? By co nverti ng all rem ainin g agric u ltura l til lag e in NZ to no -till ag e there is poten tia l to turn approx im atel y 800, 000 hec tares that are curr en tl y em itting carbon dioxid e cum ulat iv el y, into 800, 000 hec tares that are instead, se qu esterin g carbo n cum ulativel y. ? The carbon dioxid e re leas e d from approx im atel y 800, 0 00 hectares of til la ge in NZ could be abou t 2. 4 m illi on tonn es pe r year (8 00,0 00 X 3 ton nes / ha) . ? Not onl y wo uld carbo n em is s ions from agric u lt u r a l cro ps be gre atl y re duc ed un de r a no- til lag e reg im e, the carbo n bala nce for agricu ltura l crop and pas ture estab lis hm ent (carbon dioxi de em is s ions bala nced aga inst seq ues trat ion of soil carb on by de c a yi n g crop r esidu e s ) would becom e positive. ? By co ntras t, if all rem aining crop and pas t ur e esta blis h m ent is undertak en b y til la g e the carbon balanc e wil l rem ain strongl y nega tive. Prepare d b y Dr C John Bak er (Ma y 20 09) Presid ent, NZ No- T ill age Ass ociatio n