ROOT PRODUCTION AND SOIL CARBON ACCUMULATION IN ANNUAL, PERENNIAL, AND DIVERSE CROPPING SYSTEMS By Christine Dazil Sprunger A DISSERTATION !Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Crop and Soil Sciences Doctor of Philosophy Ecology, Evolutionary Biology and Behavior Dual Major 2015! ABSTRACT ROOT PRODUCTION AND SOIL CARBON ACCUMULATION IN ANNUAL, PERENNIAL, AND DIVERSE CROPPING SYSTEMS By Christine Dazil Sprunger Soil carbon (C) accumulation in agricultural landscapes can improve soil health and concurrently mitigat e climate change. My dissertation addresses three major knowledge gaps with respect to root production and soil C accumulation within agricultural landscapes: Nitrogen fertilizer additions , life history (annual versus perennial), and biodiversity. In addition, I investigate how farmers perceive soil C on their fields and determine which soil C indicators best reflect their perceptions of soil health. Planting perennial grain crops in place of annual row crops could lead to C sequestration due to their extensive root systems. In chapter s 2 and 3, I test the optimal parti tioning theory and examine soil C cycling of annual winter wheat ( Triticum aestivum ) and perennial intermediate wheatgrass ( Thinopyrum intermidum ; IWG ) under three nitrogen levels (Low N (Organic N ), Mid N, High N). I found that IWG had significantly greater root biomass at surface depths compared t o wheat (P<0.05), but there were no differences at subsurface depths between the two crops . In 2011 and 2012, total root biomass remained stable across the three N levels for both crops but in 2013, IWG root biomass in the High N level was significantly greater than in the Low N (Organic N) and Mid N levels (p<0.05). Despite si gnificantly greater root C in IWG , there were no differences in labile or recalcitrant C pools compared to wheat . Overall, these results fail to support the optimal portioning theor y and findings suggest that a longer period of time is needed in order for soil C to accumulate under perennial grain crops. The ability to sequester C could be a major benefit of perennial cellulos ic biofuel s. In chapter s 4 and 5 , I examine fine roo t production and soil C dynamics via a long -term incubation in candidate biofue l cropping systems that differ in life histories (annual vs. perennial) and diversit y (monoculture vs. polyculture) in contrasting soils . I found that the native grasses and restored prairie systems had greater root production compared to the monoculture perennials (p<0.05). At the low fertility site , I found substantial differences in active C pools between annual and perennial polyculture crops. Active C pools under polycultur es were over 2.5 times greater than under continuous corn . At the high fertility site , most system differences were insignificant except the restored prairie and rotational corn had 3.4 times more active C than other systems. I conclude that diverse perenn ial biofuel crops grown on marginal lands are more effective at C accumulation compared to diverse perennials grown on high fertility soils. In chapter 6, I com pare the total soil organic matter test to the C mineralization (active C) test to determine which soil C indicator reflected differences in management across 52 farm fields in Michigan and whether test results reflect farmer perceptions of soil C. Results from the active C test strongly supported investigator field observations and far mer perceptions of soil C. My findings demonstrate that the active C test should be widely offered at university and commercial laboratories. Overall, these results show that roots of established perennial grain crops increase with greater N addit ions, which can lead to large C stores and N retention in roots . However, in two separate experiments, I found no evidence for enhanced soil C accumulati on over the first 4 -5 years under monoculture perennial cropping systems relative to annual row-crops . This suggests that crop diversity in perennial based cropping systems should be promoted to replenish soil C for increased soil health and climate change mitigation. Copyright by CHRISTINE DAZIL SPRUNGER 2015 v Dedicated to my Parents, who have always believed in me. vi ACKNOWLEDGEMENTS The amount of generosity and support that I have received during my doctoral career is truly remarkable and I will be forever grateful. First and foremost, I would like to thank my advisor Dr. G. Philip Robertson for his support and kindness. Comple ting this PhD would not have been possible were it not for his generosity, guidance, and extensive wisdom in soil science and biogeochemistry. He has greatly influenced my development as a scientist and I am deeply appreciative of the time he has dedicated towards mentoring me. I would also like to thank the other members of my committee, Dr. Kimberly Chung, Dr. Stephen K. Hamilton, and Dr. Sieglinde Snapp. I appreciate the time and energy that each committee member has dedicated towards my developme nt as a researcher and scientist. Their contributions have strengthened my ability to identify key research questions and conduct a broad range of research. I would also like to thank my collaborators Steve Culman, Brendan they made science fun and taught me to never give up. I am also grateful for their friendships and know that we will be collaborators for life. Much of this research would not have been possible were it not for the help that I received in the field and laboratory. I thank Mark Freeman, John Green, Rich Price, Joe Simmons, Kevin Kahmark, Stacey VanderWulp, Cathy McMinn, and Sven Bohm for their guidance in the field and laboratory as well as with agronomic management and field maintenance. I would like to thank the countless technicians and undergraduate research assistants for their help with sampling and lab work. I would especially like to thank Josh Armagost and Ben Jordan for sieving roots and for their humor, which got me through the summer of 2012. I also thank Jane Schuette for help with figure preparation. I would also like to vii thank other colleagues and lab mates: Mike Abraha, Kate Glanville, Di Liang, Julie Doll, Ilya Gelfand, Bonnie McGill, Neville Millar, Adam Reimer, Sarah Roley, Suzanne Sipp el, and Danielle Zoellner for helpful feedback during lab meetings. In addition, I thank MSU Extension staff (Paul Gross and James DeDecker) and Colleen Forestieri of the Van Buren Conservation District as well as the 13 farmers that participated in the soci al science component of this dissertation. I feel extremely fortunate to have been a part of the Kellogg Biological Station community and the department of Plant, Soil, and Microbial Sciences. I would especially like to thank Dr. James Kells and Dr. Katherine Gross for their support over the past five years. In addition, I thank the administrative staff of both these departments for their kindness and assistance. My time here at Michigan State University was enjoyable because of the friendships that I made. I would especially like to thank Carolyn Lowry and Dustin Kincaid as they have always been there for me. I would also like to thank my best friends from high school and college, who have always cheered me on. I have the best family anyone could ever ask for and I immensely appreciate their love and support during my doctoral studies . Lastly, I would like to thank my funding sources: The Ford Foundation Fellowship, The Michigan State University Enrichment Fellowship, the SARE graduate student grant, and several KBS summer fellowships. viii TABLE OF CONTENTS LIST OF TABLES ........................................................................................................................ xii LIST OF FIGURES .......................................................................................................................xv CHAPTER 1: DISSERTATION INTRODUCTION ......................................................................1 OVERVIEW ..............................................................................................................................1 DISSERTATION OBJECTIVES ..............................................................................................4 CHAPTER ORGANI ZATION ..................................................................................................4 REFERENCES ..........................................................................................................................7 CHAPTER 2: ROOT ALLOCATION RESPONSES TO NITROGEN FERTILIZER IN AN ANNUAL WHEAT VERSUS PERENNIAL WHEATGRASS CROPPING SYSTEM ..............11 ABSTRACT ..............................................................................................................................11 INTRODUCTION ....................................................................................................................12 METHODS ...............................................................................................................................14 Site description ....................................................................................................................14 Experimental design ............................................................................................................15 Aboveground biomass sampling .........................................................................................16 Belowground biomass and soil sampling ...........................................................................16 Crop carbon (C) and nitrogen (N) analysis .........................................................................17 Nitrogen use efficiency .......................................................................................................17 Statistics ..............................................................................................................................17 RESULTS .................................................................................................................................18 Weather and soil m oisture ..................................................................................................18 Aboveground biomass ........................................................................................................18 Belowground bi omass .........................................................................................................19 Crop allocation and root:shoot ratios ..................................................................................19 Coarse and fine root biomass by depth ...............................................................................20 Crop N and use efficiency ...................................................................................................21 DISCUSSION ..........................................................................................................................22 Root responses to added N ..................................................................................................23 Nitrogen use efficiency in annual vs. perennial systems ....................................................26 Implications for enhanced ecosystem services by perennial grain crops ...........................27 CONCLUSIONS ......................................................................................................................28 APPENDIX .............................................................................................................................29 REFERENCES ........................................................................................................................37 CHAPTER 3: LITTLE EVIDENCE FOR EARLY SOIL CARBON CHANGE UNDER A PERENNIAL GRAIN CROP ........................................................................................................42 ABSTR ACT ..............................................................................................................................42 INTRODUCTION ....................................................................................................................43 METHODS ...............................................................................................................................45 Site description ....................................................................................................................45 ix Experimental design ............................................................................................................45 Aboveground biomass sampling .........................................................................................46 Belowground biomass and soil sampling ...........................................................................46 Crop C and N analysis ........................................................................................................47 Labile and recalcitrant C pools ...........................................................................................47 Statistics ...............................................................................................................................48 RESULTS .................................................................................................................................49 Aboveground C ....................................................................................................................49 Root C and depth distribution ..............................................................................................49 C and N concentra tions and C:N ratios ................................................................................50 Part iculate organic matter C .................................................................................................51 Power analysis .....................................................................................................................53 DISCUSSION ...........................................................................................................................53 Crop C and POM -C fractions ..............................................................................................53 Lack of increase in soil C under IWG .................................................................................55 Vision of perennial grains as a tool for soil C accumulation ...............................................57 CONCLUSIONS .......................................................................................................................58 APPENDIX ..............................................................................................................................60 REFERENCES .........................................................................................................................72 CHAPTER 4: CHANGES IN ACTIVE AND SLOW SOIL CARBON POOLS UNDER PERENNIAL BIOENERGY CROPS IN CONTRASTING SOILS .............................................77 ABSTRACT ..............................................................................................................................77 INTRODUCTION ....................................................................................................................78 METHOD S ...............................................................................................................................81 Site description ....................................................................................................................81 Experimental design and systems .......................................................................................82 Soil sampl ing ......................................................................................................................83 Long -term incubations ........................................................................................................84 Acid hydrolysis ...................................................................................................................84 The three pool model ..........................................................................................................85 Particulate organic matter ...................................................................................................86 Statistics ..............................................................................................................................87 RESULTS .................................................................................................................................87 Surface cumulative fluxes ...................................................................................................87 Subsurface cumulative fluxes .............................................................................................88 Cumulative flux per soil C ..................................................................................................88 Trends of surface fluxes over time ......................................................................................89 The active C pool ................................................................................................................89 The slow C pool ..................................................................................................................90 Non-hyd rolyzable (resistant) C pool ...................................................................................91 Mean residence time ...........................................................................................................91 Partic ulate organic matter fractions ....................................................................................92 DISCUSSION ...........................................................................................................................92 Active C pool ......................................................................................................................93 x Slow C pool .........................................................................................................................96 Passive C pool .....................................................................................................................97 Little evidence for active C at lower depths ........................................................................98 Particulate organic matter patterns .......................................................................................99 Management implications ..................................................................................................100 CONCLUSIONS .....................................................................................................................101 APPENDIX ............................................................................................................................102 REFERENCES .......................................................................................................................124 CHAPTER 5: PLANT DIVERSITY INFLUENCES FINE ROOT PRODUCTION AND BIOMASS ALLOCATION AMONG PERENNIAL BIOFUEL CROPPING SYSTEMS IN CONTRAST ING SOILS OF THE UPPER MIDWEST, USA ...................................................130 ABSTRACT ............................................................................................................................130 INTRODUCTION ..................................................................................................................131 METHODS .............................................................................................................................133 Site description ..................................................................................................................133 Experimental design and s ystems .....................................................................................133 Fine root production ..........................................................................................................135 Aboveground net primary production ...............................................................................136 Fine Root BNPP:ANPP index ..........................................................................................137 Root depth distribution .....................................................................................................137 Statistics ............................................................................................................................137 RESULTS ................................................................................................................................138 Precipitation ......................................................................................................................138 Fine root production ..........................................................................................................138 Fine root BNPP:ANPP index ............................................................................................140 Late season vs. mid -season fine root production ..............................................................141 Root depth distribution .....................................................................................................142 DISCUSSION .........................................................................................................................143 Diversity influences mid -season fine root production and allocation ..............................143 Belowground allocation ....................................................................................................145 Implications for carbon sequestration ...............................................................................146 Timing of peak fine root production .................................................................................147 Root depth distribution .....................................................................................................148 CONCLUSIONS .....................................................................................................................148 APPENDIX .............................................................................................................................150 REFERENC ES .......................................................................................................................160 CHAPTER 6: DO TOTAL SOIL CARBON TESTS MEET FARMER MANAGEMENT NEEDS? MEASURES OF ACTIVE CARBON VERSUS STATIC SOIL ORGANIC MATTER POOLS .........................................................................................................................................165 ABSTRACT ............................................................................................................................165 INTRODUCTION ..................................................................................................................166 METHODS .............................................................................................................................168 xi Participant selection ...........................................................................................................168 On-farm soil sampling and field observations ...................................................................169 Laboratory analyses: total soil organic matter (SOM) and active C ..................................170 Individual farmer meetings and qualitative analyses .........................................................171 Statistics .............................................................................................................................171 RESULTS ...............................................................................................................................171 Variability in total SOM and active C across farmer fields ...............................................171 Differences between Best and Worst Fields: total SOM versus a ctive C ..........................172 Penetration resistance .........................................................................................................172 Field observations versus active C .....................................................................................173 Farmer perceptions of soil health versus active C .............................................................173 The importance o f SOM and associated challenges ..........................................................174 Motiva tions for building SOM ...........................................................................................176 Managem ent practices used to build SOM ........................................................................176 Linking soil tests to management a nd expressed interest in soil health testing .................179 DISCUSSION ..........................................................................................................................181 Active C vs. SOM test results .............................................................................................181 Do active C test results support field observations and farmer perceptions? .....................182 Bridging the gap between scientific testing and farmer knowledge ...................................183 Future directions .................................................................................................................185 CONCLUSIONS ......................................................................................................................185 APPENDIX .....187 REFERENCES ........................................................................................................................210 xii LIST OF TABLES Table 2.1 Gravimetric soil moisture at five depths throughout the soil profile in wheat and IWG in 2011, 2012, and 2013, averaged across N levels (means ± se). Different superscript letters within years denote significant differences between crops for each depth and year combination at (p< 0.05) ............................................................................30 Table 2.2 Total biomass in wheat and IWG across three N levels in 2011, 2012, and 2013 (means ± se). Comparisons of cropping system means within a given year followed by same superscript letters are not significant. Different letters within a col umn of a given year denotes significant differences across N level. ................................................31 Table 2.3 Total N Content in wheat and IWG across three nitrogen levels in 2012 and 2013 (means ± se). Comparisons of means within rows (among cropping system) followed by same lowerca se letters are not significant. Different letters within a column of a given year denotes significant differences across N level. ................................................32 Table 2.4 Nitrogen Use Efficiency in Harvested N, Root N, and Total Plant N. NUE ratios greater than 1.0 indicate that the plant took up more N than what was applied during the growing season. Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significant. Different letters within a column of a given year denotes significant differences across N level. ............................33 Table 3.1 Grain and straw between wheat and IWG across three N levels (Low N (Organic N), Mid N, and High N). Comparisons of means withi n rows (among cropping system) followed by same lowercase letters are not significantly different. Different lower case letters denote significant differences bet ween crops and across N levels .........................61 Table 3.2 Coarse and fine roots between wheat and IWG acros s three N levels (Low N (Organic N), Mid N, and High N). Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significantly different. Different lower case letters denote significant differences betwe en crops and across N levels .........................62 Table 3.3 Carbon concentrations for coarse fine root biomass across N levels (Low N (Organic N), Mid N, and High N) at five depths. Comparisons of means within rows (among cropping system) followed by same lowercase letters are no t significant. Different lower case letters denote significant di fferences between wheat and IWG .......63 Table 3.4 Nitrogen concentrations for coarse fine root biomass across N levels (Low N (Organic N), Mid N, and High N) at five depths. Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significant. Different lower case letters denote significant di fferences be tween wheat and IWG .......64 xiii Table 4.1 Mean Residence Times for surface soils (0 -10 cm) of ten biofuel crop ping systems at ARL for the active and slow C pool. ...........................................................................103 Table 4.2 Mean Residence Times for surface soils (0 -10 cm) of ten biofuel cropping systems at KBS for the active and slow C pool. ............................................................................104 Table 4.3 Surface soil (0-10 cm) depth particulate organic matter C concentrations (means and standard errors) for ten biofuel cropping systems at ARL and KBS. .......................105 Table 4.4 Particulate organic matter C concentrations from 10 -25 cm, 25 -50 cm, 50 -100 cm depths (means and stan dard errors) for four biof uel cropping systems at ARL and KBS. ..................................................................................................................................106 Table 5.1 Shannon -Weiner diversity index for native grass, early successional, and restored prairie s ystems at KBS and ARL for years, 2011, 2012, and 2013. ................................151 Table 5.2 Difference between end of season fine root production and mid -season fine root production at ARL and KBS. Numbers represent the mean and standard error (in parentheses) for each system. A positive number indicates greater root production at in the later part of the growing season and a negative number indicates greater root production during the middle of the growing season. ......................................................152 Table 6.1 Type and scale of participating farms in Michigan State. ...........................................188 Table 6.2 Mean total SOM and Active C for all fields and paired t -tests and mean CVs for field comparison across 13 farms in Michigan ................................................................189 Table 6.3 Penetrometer resistance (psi) in four fields from each farm (means !± SE) . Instances where areas in the field exceeded the maximum resistance, values are .........................................190 Table 6.4 Fields in addition to C flux trends in Isabella, Pres que Isle and Van Buren Counties ....191 Table 6.5. Summary and frequency of farmer field descriptions for Best and Worst Fields ......192 Table 6.6 . Choice field farmer descriptions separated by performance (Good performing field, intermediate field, problematic field) as described by the farmer across 13 farms ................................................................................................................................193 Table 6.7 Management practice thematic categories and selected examples of approaches that farmers use t o build soil organic matter. ...................................................................194 Tabl e 6.8 Investigator Fields in addition to C flux trends in Isabella, Presque Isle and Van Buren Counties. ...195 xiv Table 6.9 Farmer descriptions and experiences of Best, Worst, and Choice fiel ds and C flux trends in Isabella County. ................................................................................................203 xv LIST OF FIGURES Figure 2.1 Root:shoot ratios of wheat and IWG in 2011, 2012, and 2013 for over three N levels ( Low N (Organic N), Mid N, and High N). The sum of total coarse and total fine roots were used to calculate total root biomass. Total straw and grain were summed to determine total shoot biomass. Error bars represent the standard error of the mean and different letters d enote significance at <0.05. .................................................34 Figure 2.2 Coarse root biomass values in annual winter wheat (triangle) and IWG (circle) for three management practices ( Low N (Organic N), Mid N, and High N) over three years (2011, 2012, 2013) at five different depths thro ughout the soil profile. Error bars represent the standard error of the mean and asterisks denotes significance at <0.05, t denotes significance at <0.1. ....................................................................................35 Figure 2.3 Fine root biomass values in annual winter wheat (triangle) and IWG (circle) for three management practices ( Low N (Organic N), Mid N and High N) over three years (2011, 2012, 2013) at five different depths throughout the soil profile. Error bars represent the standard error of the mean and asterisks denotes significance at <0.05, t denotes significance at <0.1. ....................................................................................36 Figure 3.1 Coarse root C content for annual winter wheat (triangle) and IWG (circle) for three N levels (Low N (Organic N), Mid N and High N) at five different soil depths. Error bars represent the standard err or of the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. .......................................................................65 Figure 3.2 Fine root C in annual winter wheat (triangle) and IWG (circle) for three N levels (Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error of the mean and asterisks denote signifi cance at p<0.05 and t denotes significance at p<0.1. ..................................................66 Figure 3.3 Coarse root C:N ratios for annual winter wheat (triangle) and IWG (circle) for three N levels (Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error of the mean and asterisks denotes significance at <0.05 and t denotes significance at <0.1. ..........................67 Figure 3.4 Fine root C :N ratios for annual winter wheat (triangle) and IWG (circle) for three N levels ( Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error o f the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. ........................68 Figure 3.5 Large and Medium POM -C concentra tions for annual winter wheat (triangle) and IWG (circle) for three N levels (Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error of xvi the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. .....................................................................................................................................69 Figure 3.6 Large and Medium POM -C Content for annual winter wheat (triangle) and IWG (circle) for three N levels (Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error of the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. .................70 Figure 3.7 Probability (power) of detecting a statistical ly significant difference in surface (0 -10 cm) POM -C between wheat and IWG at (p=0.05) for two scenarios: 1) increasing the number of replicates but keeping the observed difference in POM -C between wheat and IWG, and 2) keeping the same number of replicates (n=4) but using a hypothesized increase in POM -C (15 %). ..............................................................................71 Figure 4.1 C umulative C mineralizaton from surface soils (0 -10 cm depths) over the course of 322 day incubations for ARL (n=3). Systems with different lowercase letters are statistically different from one another (p <0.05). .................................................................107 Figure 4.2 Cumulative C mineralizaton from surface soils (0 -10 cm depths) over the course of 322 day incubations for KBS (n=3). Systems with different lowercase letters are statistically different from one another (p <0.05). .................................................................108 Figure 4.3 Cumulative C mineralization for corn, switchgrass, native grasses, and restored prairie gradient at 0 -10 cm. 10 -25 cm, 25 -50 cm, and 50 -100 cm depths . Within each site and depth interval, systems with different lowercase letters are statistically different from one another (p <0.05).. ...................................................................................109 Figure 4.4 C umulative C mineralizaton per gram of soil C from surface soils (0 -10 cm depths) over the course of 322 day incubations for ARL and KBS (n=3). For each site, systems with different lowercase letters are statistically different from one another ( p <0.05). ...................................................................................................................110 Figure 4.5 The active C pool for surface soils (0 -10cm). Within each site, systems with different lowercase letters are statistically different from one another (p <0.05). Bars with no letters were not significantly different from one anothe r. Bars are means ± SE. ..........................................................................................................................................111 Figure 4.6 The slow C pool for surface soils (0 -10cm). Within each site, systems with different lowercase letters are statistically different from one another (p <0.05). Bars with no letters were not significantly different fro m one another. Bars are means ± SE. ..........................................................................................................................................112 Figure 4.7 The resistant C pool determined by acid hydrolysis and averaged across cropping system. For each depth interval, asterisks represent statistically significant differences across site (p <0.05). Bars with no asterisk were not significantly different from one another. Bars are means ± SE. .................................................................113 xvii Figure 4.8 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the continuous corn system at ARL. Shaded bands represent standard err or from the mean. .......................................................................................................................114 Figure 4.9 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the corn -soybean -canola system at ARL. Shaded bands represent standard error from the mean. ..............................................................................................................114 Figure 4.10 Predicted mean C mineraliza tion over the 322 day incubation period (dotted curve) for the soybean -corn -canola system at ARL. Shaded bands represent standard error from the mean. ..............................................................................................................115 Figure 4.11 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for th e canola -corn -soybean system at ARL. Shaded bands represent standard error from the mean. ..............................................................................................................115 Figure 4.12 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the switchgrass system at ARL. Shaded bands represent standard error from the mean. .......................................................................................................................116 Figure 4.13 Predicted mean C mineralization over the 322 day incubation period ( dotted curve) for the miscanthus system at ARL. Shaded bands represent standard error from the mean. .......................................................................................................................116 Figure 4.14 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the poplar system at ARL. Shaded bands represent sta ndard error from the mean. ......................................................................................................................................117 Figure 4.15 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the native grass system at ARL. Shaded bands represent standard error from the mean. .......................................................................................................................117 Figure 4.16 Predicted mean C mineral ization over the 322 day incubation period (dotted curve) for the early successional system at ARL. Shaded bands represent standard error from the mean. ..............................................................................................................118 Figure 4.17 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the restored prairie system at ARL. Shaded bands represent standard error from the mean. .......................................................................................................................118 Figure 4.18 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the continuous corn system at KBS. Shaded bands represent standa rd error from the mean. .......................................................................................................................119 xviii Figure 4.19 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the corn -soybean -canola system at KBS. Shaded bands represent standard error from the mean. ..............................................................................................................119 Figure 4.20 Predicted mean C min eralization over the 322 day incubation period (dotted curve) for the soybean -corn -canola system at KBS. Shaded bands represent standard error from the mean. ..............................................................................................................120 Figure 4.21 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the canola -corn -soybean system at KBS. Shaded bands represent standard error from the mean. ..............................................................................................................120 Figure 4.22 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the switchgrass system at KBS. Shaded bands represent standard error from the mean. .......................................................................................................................121 Figure 4.23 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the miscanthus system at KBS. Shaded bands represent standard error from the mean. .......................................................................................................................121 Figure 4.24 Predicted mean C minera lization over the 322 day incubation period (dotted curve) for the poplar system at KBS. Shaded bands represent standard error from the mean. ......................................................................................................................................122 Figure 4.25 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the native grass system at KBS. Shaded bands represent standard error from the mean. .......................................................................................................................122 Fig ure 4.26 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the early successional system at KBS. Shaded bands represent standard error fro m the mean. ..............................................................................................................123 Figure 4.27 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the restored prairie system at KBS. Shaded bands represent standard error from the mean. .......................................................................................................................123 Figure 5.1 Precipitation during two different i ntervals of the growing season for which in -growth cores were installed . Beginning to Mid season started when the cores were installed in mid April and ended when the f irst set of cores were removed during the middle of the growing season. The Mid to Late season interval covers the length of time between the mid season core removal and the date when the second set of cores were removed at the end of the growing season. ...................................................................153 Figure 5.2 Mid-season fine root production (geometric mean) for six perennial croppi ng systems ranging in diversity (switchgrass, miscanthus, poplar, native grasses, early xix successional, and restored prairie) at A RL in 2011, 2012, and 2013. Error bars represent back transformed 95% confidence intervals. .........................................................154 Figure 5.3 Mid-season fine root production (geometric mean) of six perennial cropping systems ranging in diversity (switchgrass, miscanthus, poplar, native grasses, early successional, and restored prairie) at the KBS in 2011, 2012, and 2013. Error bars represent bac k transformed 95% confidence intervals. .........................................................155 Figure 5.4 BNPP: ANPP Index (geometric mean) of six perennial cropping systems ranging in diversity (switchgrass, miscanthus, poplar, native grasses, early successional, and restored prairie) at ARL in 2011, 2012, and 2013. BNPP :ANPP ind ices are the ratio of fine root production to ANPP. Error bars represent back transformed 95% confidence intervals. ..............................................................................................................156 Figure 5.5 Root: ANPP Index (geometric mean) of six perennial cropping systems ranging in divers ity (switchgrass, miscanthus, poplar, native grasses, early successional, and restored prairie) at KBS in 2011, 2012, and 2013. Error bars represent back transformed 95% confidence intervals. ..................................................................................157 Figure 5.6 A) Miscanthus and Switchgrass root biomass dist ribution averaged across three years (2011, 2012, and 2013) to one meter at KBS and Arlington. B) Poplar root biomass distribution to 1 meter from the nearby Long -Term Ecologi cal Research experiment at KBS. ................................................................................................................158 Figure 5.7 Timing of in -growth core installa tion and p recipitation at ARL and KBS during 2011, 2012, and 2013. Both sets of cores were installed in late March or early Apr il. The first set of cores (Mid -season) were typically removed in mid July or early August and the second set of cores ( late seas on) were removed in late October or early November. Arrows indicate when cores were installed and removed. a)=ARL 2011, b)ARL 2012, c) ARL 2013, d) KBS 2011, e) KBS 2012, f) KBS 2013. .....................159 Figure 6.1 Percent difference for Total SOM and C mineralization between Best and Worst fields across 13 farmer field in Michigan. .............................................................................209 1 CHAPTER 1: DISSERTATION INTRODUCTION OVERVIEW Society is currently facing several unprecedented challenges including global climate change, historic losses of arable land, fuel insecurity, and malnutrition with over 1 billion food insecure people worldwide (FAO, 2009; Lal, 2011). Current atmospheric CO2 concentrations are at 398 ppmv and are projected to increase at a rate of 2.2 ppm v/yr (IPCC, 2007). Projections for 2050 (FAO, 2009) and demands for food will prompt eve n more land conversion and intensive agriculture . Food insecurity and global climate change are intertwined as both of these challenges can be alleviated or further exacerbated depending on soil carbon (C) management (Lal, 2010). At the farm scale, so il C provides ecosystem services by increas ing soil health and crop productivity. For example, an increase in soil C can improve water holding capacity, regulate nutrient cycling and retention, enhance soil physical structure, provide a better medium for plant roots to obtain water and nutrients by re ducing porosity, and increase soil biodiversity (West and Post, 2002; Johnston et al., 2009). The importance of soil C can also be realized at the global scale as soils contain between 1500-2000 Pg of C (Janzen, 2004). The exchange of CO 2 between t errestrial landscapes and the atmosphere has a major role in regulating the global C cycle. CO 2 is assimilated into the terrestrial biome through photosynthesis; however half of this CO 2 is soon released back to the atmosphere through plant respiration (Sc hlesinger, 1997). Globally, soils hold twice the amount of C that is found in the atmosphere and thus serve as an important C pool (Swift, 2001). However, due to land conversion and intensive agricultural practices soil C has been reduced by up to 75 % in agricultural landscapes (Lal, 2010). The consequences of soil C loss 2 include reductions in soil health and crop productivity, as well as enhanced CO2 emissions. Currently, CO 2 emissions from land use change account for approximately 17 % of total GHG emissions caused from anthropogenic acti vities (IPCC, 2007 ) and historically they account for approximately 124 Pg C of CO2 emissions to the atmosphere between the years 1850 and 1990 (Houghton, 1998). The f actors needed to replenish the soil C pool include increasing soil organic matter inputs while slowing decomposition of C inputs , placing soil C deeper in the ground where there is reduced microbial activity, and enhancing the physical protection of C through aggregation (Post and Kwon, 2000). However, increasing soil C is challenging because the total C pool is large and dynamic and consists of different pools that vary in turnover times (Paul, 2001; Wander, 2004). The active C pool consists of freshly decomposing material and has a mean reside nce time of up to a year, while the slow C pool consists of material that is more lignified and typically has a mean residence time of a few decades. The resistant pool is the largest and oldest pool of C and mainly consists of inorganic and non -hydrolyzab le organic C. Since the resistant pool reflects the largest and most recalcitrant pool of total C, it often takes decades to detect differences in soil C following a change in management. Restoring C pools in agricultural systems is attractive beca use increasing soil C can lead to healthier soils and increase crop production while simultaneously mitigating climate change through C sequestration. Furthermore, there are several management practices that have proven to be effective at sequestering C ov er time. For instance, utilizing no -till management in place of conventional tillage can result in sequestration rates of 57 g C m -2 yr-1 and incr easing rotational complexity has been found to sequester 20 g C m -2 yr-1 (West et al., 2002). Sainju et al. (2 008) found that poultry additions lead to C seq uestration rates of 510 kg C ha -1 yr-1. Perhaps the 3 management strategy that has proven to be most effective for C sequestration is converting agricultural systems back to perennial vegetation (Post and Kwon, 2000; Syswerda et al., 2011). Converting annual row crop systems to perennial vegetation or successional systems has resulted in sequestration rates of up to 60 g C m -2 yr-1 (Council for Agricultural Science and Technology, 2004). Perennial systems are effective at sequestering soil C due to their extensive root systems, year -round ground cover, and lack of disturbance after the initial cultivation (Post and Kwon, 2000; Glover et al. , 2010). Perennial systems tend to have at least three times more ro ot biomass than annual systems (Dupont et al. , 2014). Since root production and decay represent the primary source of C in most terrestrial ecosystems, perennial systems with extensive roots can be major contributors to soil C sequestration. Furthermore, r oots tend to persist in soil longer than aboveground material and can thus play a key role in C stabilization (Kong and Six, 2010; Rasse et al., 2005). Thus, the development of perennial cropping systems for food or fuel is attractive. Two relatively new options for incorporating perennial crops into agricultural landscapes include the development of perennial grain crops and perennial cellulosic biofuels. Breeders are working to develop perennial grain crops that achieve yields comparable to an nual r ow crops with extensive roots that could provide ecosystem services. The concept of perennial cellulosic biofuels has gained an increasing amount of traction since the U.S. C ongress mandated that 136 billion liters of renewable fuel be produced annually by the year 2020 (Sissine, 2007). Currently, the main source of bioethanol production is corn, which has capped at 56.8 billion liters (Sissine, 2007), indicating that other biofuel sources are needed to meet Energy Independence and Security Act requirements . Furthermore, perennial cellulosic biofuels are more attractive than corn production due to their C sequestration p otential (Lemus and Lal, 2005). 4 DISSERTATION OBJECTIVES My overall objective is to determine root production and C accumulation in annual a nd perennial crops used for food or fuel that receive different amounts of fertilizer and vary in biodiversity. The focal questions that I address include : How do organic and inorganic sources of N impact above and belowground biomass allocation and C stor age? Do perennial crops for both food and biofuel cropping systems enhance labile and recalcitrant C? How does biodiversity influence fine root production and C accumulation in active, slow, and resistant pools? Which soil C tests detect changes in managem ent across farmer fields and align with farmer perceptions of soil C? To address these questions, I utilize methods from soil science, biogeochemistry, agroecology, and qualitative social -science research . CHAPTER ORGANIZATION To date, much of the research regarding perennial grain crops has been devoted to breeding efforts and aboveground productivity ( Jaikumar et al., 2012; Murphy et al., 2010 ), while no study has investigated belowground production in situ. Moreover, littl e is known about the effect of fertilizer types and rates on biomass allocation and vertical root distribution in annual or perennial cropping systems. Although empirical field data regarding the effects of fertilizer on biomass allocation and root productio n are scarce, understanding plant resource allocation in nutrient rich and nutrient poor systems is a concept that has received widespread attention in ecology. For example, the optimal partitioning theory posits that systems where essential nutrients are lacking will have increased belowground production and in cases where excess fertilizer is added, root biomass will decrease (Bloom, 1985). If increased fertilizer leads to reductions in root growth, important ecosystem services provided by roots could ult imately be lost. In chapter 2, I test the optimal portioning theory by comparing plant biomass allocation and 5 coarse and fine root production of annual winter wheat (Triticum aestivum L. var. Caledonia) and perennial intermediate wheatgrass [(Thinopyrum intermedium (Host) Barworkth and D.R. Dewey); IWG] across a Nitrogen (N) fertilizer gradient. I also examine vertical root distribution to determine if perennial root biomass increases at depth in systems receiving lower rates of N compared to annuals due to more pers istent roots. Finally, I quantify whole -plant nitrogen use efficiency across the different N levels in wheat and IWG. Initial results of aboveground productivity in IWG reveal that yields are low and decline further in their third or fourth year (Cu lman et al. , unpublished). Since C sequestration is one of the main motivations for the development of perennial grains, it is important to understand how much time is required before soil C starts to accumulate in these systems. The decline in yields afte r three years would require farmers to either replant or switch to another crop, in which case C sequestration in these systems might never be realized. In chapter 3, I compare coarse and fine root C mass of wheat and IWG down to a 1 m depth as well as lab ile and recalcitrant C pools to determine if IWG accumulates more soil C relative to wheat four years after establishment. In contrast to chapters 2 and 3 where I compare belowground C dynamics of a monoculture annual and perennial gr ain system, in chapters 4 and 5 , I examine belowground production and C accumulation in annual and perennial biofuel cropping systems differing in diversity. The effects of crop diversity on aboveground productivity have been extensively studied and are well known; t ypically crop diversity leads to increased aboveground productivity, especially in low fertility systems (Tilman, 1996; Smith et al., 2008 ). The effects of crop diversity on belowground production are less well known. Despite this knowledge gap, several hy potheses posit that crop diversity will lead to increased root production and C accumulation. For example, Hooper and Vitousek (1997 ) suggest that root production will be 6 greater in more diverse cropping systems due to plant complementarity effect s and differences in phenology and nutrient demand . de Kroon et al. (2012) hypothesize that pathogens constrain root growth in monocultures compared to mixed species communities and that due to competition for nutrients, root production in mixed species systems wil l be more extensive. Given the wide variety of candidate biofuel cropping systems, understanding how species composition and functional diversity influences belowground C dynamics is crucial for determining short and long term C sequestration potentials i n these systems. Scientists and policy makers strongly encourage farmers to adopt sustainable management practices that could result in soil health improvements and C sequestration. However, farmers largely base their management decisions on soil test results. To date, total Soil organic matter ( SOM ) is the most common soil C indicator used by farmers but often times total SOM is not sensitive to short -term management changes (Culman, 2013). C mineralization (active C) , a test that is more sensitiv e to changes in management and reflects the labile soil C pool, is not widely offered at university and commercial laboratories. In chapter 6, I combined soil science field -based research with qualitative social -science methodology to determine if the acti ve C test is able to detect differences across fields varying in soil health and performance and how well measured active C reflects farmer perceptions of soil C compared to the total SOM measurement . 7 REFERENCES 8 REFERENCES Bloom, A.J., Chapin, F.S. , Harold, A. M. 1985. Resource limitation in plants -an economic analogy. Annual Re view of Ecology and Systematics. 16:363392. Culman, S.W., S. S. Snapp., C.S. Sprunger., A. L, Peralta., L.R., DeHaan. In prep. Enhanced ecosystem services under perennial intermediate wheatgrass compared to annual winter wheat. Culman, S. W., S. S. Snapp, J. M. Green, and L. E. Gentry. 2013. Short - and long -term labile soil carbon and nitrogen dynamics reflect management and predict corn agronomic performa nce. Agronomy Journal . 105(2) :493502. de Kroon, H., M. Hendriks, J. van Ruijven, J. Ravenek, F. M. Padilla, E. Jongejans, E. J. W. Visser, and L. Mommer. 2012. Root responses to nutrients and soil biota: drivers of species coexistence and ecosystem produc tivity. Journal of Ecology . 100:615. DuPont, S. T., J. Beniston, J. D. Glover, a. Hodson, S. W. Culman, R. Lal, and H. Ferris. 2014. Root traits and soil properties in harvested perennial grassland, annual wheat, and never -tilled a nnual wheat. Plant and S oil. 381: 405420. FAO. 2009. G lobal agriculture towards 2050. High -level expert f orum. Rome. Houghton, B. R. A., T. Woods, P. O. Box, and W. Hole. 1998. The annual net flux of carbon to the atmosphere from c hanges in land use 1850 1990. Tellus. 51B:298 313. IPCC. 2007. Contribution of working group III to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. Jaikumar, N. S., S. S. Snapp, K. Murphy, and S. S. Jones. 2012. Agronomic assessment of perennial wheat and perennial rye as cereal crops. Agronomy Journal . 104:1716 1726. Janzen, H. 2004. Carbon cycling in earth systems --a soil science perspective. Agricultur e, Ecosystems & Environment . 104:399417. Johnston, A. E., P. R. Poulton, and K. Coleman. 2 sustainable agriculture and carbon dioxide f luxes. Advances in Agronomy . 101(8): 1-55. Kong, A. Y. Y., and J. Six. 2010. Tracing Root vs. Residue carbon into soils from convention al and alternative cropping s ystems. Soil Sci ence Society of America Journal. 74(4):1201-1210. Lal, R. 2010. Beyond Copenhagen: Mitigating climate change and achieving food security through soil carbon sequestration. Food Security . 2:169-177. 9 Lal, R. 2011. Sequestering carbon in soils of agro -ecosystems. Food Policy . 36: S33 S39. Lemus, R ., and R. Lal. 2005. Bioenergy crops and carbon s equestration. Critical Reviews in Plant Sciences . 24:121. Murphy, K., S. Lyon, K. Barlow, and S. Jones. 2010. P ost-sexual cycle regrowth and grain yield in Thinopyrum elongatum x Triticum aestivum amphiploids. Plant Breeding. 129:480-483. Paul, E. A. H. P. Collins, and S. W. Leavitt. 2001. Dynamics of resistant soil carbon of midwestern agricultural soils measured by naturally occurring 14C abundance. Geoderma 104:239256. Post, Wilfred M. and K. Kwon. 2000. Soil carbon sequestration and land use change: processes and pot ential. Global Change B iology . 6:317-327. Rasse, D. P., C. Rumpel, and M.F. Dignac. 2005. Is so il carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant and Soil . 269:341 356. Sainju, U. M., Z. N. Senwo, E. Z. Nyakatawa, I. A . Tazisong, and K. C. Reddy. 2008. Soil carbon and nitrogen sequestration as affected by long -term tillage, cropping systems, and nitrogen fertilizer sources. Agricu lture, Ecosystems & Environment. 127:234240. Schlesinger WH. 1997. Biogeochemistry: an analysis of global change, 2 nd ed. New York, NY: Academic Press. Sissine, F. 2007. Energy Independence and Se curity Act of 2007: A summary of major provisions. Washington, D.C. Congressional Research Service. Smith, R. G., K. L. Gross, and G. P. Robertson. 2008. Effects of crop diversity on agroecosystem function: Crop yield response. Ecosystems. 11:355366. Syswerda, S. P., A . T. Corbin, D. L. Mokma, a. N. Kravchenko, and G. P. Robertson. 2011. Agricultural management and soil carbon storage in surface vs. deep layers. Soil Science Society of America Journal . 75:92-101. Swift, R.S. 2001. Sequestration o f carbon by soil. Soil Science Society of America Journal. 166: 835-858. Tilman, D., P. B. Reich, and J. M. H. Knops. 2006. Biodiversity and ecosystem stability in a decade -long grassland experiment. Nature . 441:62932. Wander, M. 2004. Soil organic matter fractions and their relevance to soil function. Pg. 67 -102. In: F. Magdoff and R.R. Weil, editors, Soil organic matter in sustainable agriculture. CRC Press, Boca Raton, FL. 10 West, Tristram O. and Post, W. M. 2002. Soi l organic carbon sequestration rates by tillage and crop r otation: analysis, a global d ata. Soil Science Society of America . 66:19301946. 11 CHAPTER 2: ROOT ALLOCATION RESPONSES TO NITROGEN FERTILIZER IN AN ANNUAL WHEAT VERSUS PERENNIAL WHEATGRASS CROPPING SYSTEM ABSTRACT Perennial cropping systems typically exhibit extensive root systems that have been shown to contribute to important ecosystem services. Optimal partitioning theory predicts that plants that lack access to essential soil nutrients increase belowground productivity for root foraging potential and plants that receive excessive nutrients reduce belowground biomass and productivity and instead allocate resources aboveground. To test this theory, I quantified biomass distribution, crop bioma ss allocation, and whole crop nitrogen use e fficiency (NUE) in annual winter wheat (Triticum aestivum L. var. Caledonia) and perennial intermediate wheatgrass (IWG) , Thinopyrum intermedium (Host) Barworkth and D.R. Dewey across three nitrogen levels . The N levels were Low N ( Organic N) (90 kg N ha -1 of poultry manure ), Mid N (90 kg N ha-1 of urea) , and High N (135 kg N ha -1 of urea). In the first two years, N level had no effect on coarse (p>0.05, n=4) or fine root biomass (p>0.05 n=4) in either crop . In year three, when IWG was fully established, both coarse and fine root biomass were significantly greater under the High N addition (p<0.05, n=4) in the surface 0 -10 cm depth . There were no differences in root biomass at lower depths across N level s (P>0.05, n=4) . IWG had significantly greater root biomass compared to wheat to 40 cm (p<0.05, n=4) but no differences were found between the two crops at deeper depths. Root:shoot ratios remained stable across the three N levels in both wheat and IWG systems (P>0.05, n=4). Regardless of N level, however, IWG always had greater whole crop NUE compared to wheat (P<0.05,n=4). NUE did not significantly differ across N level for wheat , while IWG was most efficient in the Mid N system (p<0.05, n=4). I thus found 12 no evidence for root foraging in the systems receiving less N and overall results fail to support optimal portioning theory: coarse and fine root production either remained the same or inc reased with higher levels of N and was proportional to aboveground production . INTRODUCTION Perennial grains have been promoted to meet food security demands while providing important ecosystem services in agriculture (Glover, 2010). In contrast to annual crops, perennials have extensive root systems and year -round ground cover, which can be important for the delivery of numerous ecosystem services (Snapp et al., 2015 ; Syswerda and Robterson, 2014; Glover et al., 2010). For example, perennial cropping systems have been shown to reduce nitrate leaching by up to 90% compared to annual crops ( Syswerda et al., 2012; Culman et al., 2013). Furthermore, perennial systems are more efficient at building soil organic matter and reducing erosion ( McLauchlan et al., 2006; Syswerda et al., 2011). In order for perennial grain systems to achieve full yield potential, farmers will need to apply N fertilizer, which will likely influence belowground biomass allocation. Optimal partitioning theory ( OPT: Bloom et al., 1985) posits that plants will respond to nutrient limited environments by increasing root productivity while allocating less energy to aboveground crop components. In cases where access to nutrients is adequate or excessive, OPT predicts a reduction in root production and an increase aboveground (grain and shoot) production. Bloom et al. (1985 ) predict that cropping systems receiving heavy fertilizer additions will ultimately reduce their root:shoot ratios. If predictions from OPT are extended to perennial grain crops, fertilization could result in reduced ecosystem services (e.g., belowground C inputs and nutrient capture ) sought with perennial crops . 13 Belowground crop responses to N fertilizers in both annual and perennial crops are poorly understood and contradictory. Fo r example, Jarchow et al. (2012 ) found support for OPT and reported lower root production in systems receiving greater N additions in both annual and perennial mixed grass systems. In contrast, Hegenstaller et al. (2009) reported that third and fourth year switchgrass ( Panicum virgatum L.) and big bluestem ( Andropogon gera rdii Vitman ) stands had greater root biomass with increased fertilizer additions, while eastern gamagrass [Tripsacum dactyloides (L.) L.] consistently had reduced root biomass with increased fertilizer. Others have fou nd no root biomass response to increased inorganic fertilizer additions in corn and switchgrass systems (Russell et al., 2009; Jung and Lal, 2011 ). Roots are dynamic and plastic by nature and are affected by nutrient resources and water availability throughout the soil profile. Since resource limitation becomes more apparent at depth, OPT may be more relevant to perennials because they have longer growing seasons, and their roots spend a greater proportion of the year at depth compared to annual syste ms. For example, in drought conditions, fine roots extend to greater depths in order to obtain water and nutrients (Poorter and Nagel, 2000; Canadell et al., 2006). Thus, root responses to nutrient resource limitation might primarily occur at subsurface de pths. However, previous work examining root response to N additions has typically only measure d root biomass in surface horizon s (Offocer, et al., 2009; Jung and Lal, 2011) and in cases where subsurface horizons have been sampled, authors rarely report roo t biomass by depth ( Hegenstaller et al., 2009; Russell et al., 2009). Understanding how different rates and sources of N fertilization influence belowground productivity by depth and overall crop N uptake in annual versus perennial cropping systems could h ave important implications for agronomic productivity and whole crop nitrogen use -efficiency (Dawson et al. , 2008). 14 Here I compare crop biomass allocation, coarse and fine root vertical distribution, and nitrogen use efficiency (NUE) in annual winter wheat (Triticum aestivum var. Caledonia) and a novel perennial grain, perennial intermediate wheatgrass (IWG) , Thinopyrum intermedium (Host) Barworkth and D.R. Dewey across a nitrogen fertilizer gradient. Consistent with OPT theory, I hypothesize d that i) Root biomass and root:shoot ratios of both annual wheat (wheat) and perennial IWG (IWG) will decrease under increased fertilizer additions ; and ii) IWG will have a greater response to lower N at subsurface depths than will wheat, because of a more pers istent root system. Furthermore, I hypothesize that both wheat and IWG whole crop NUE will be reduced in systems receiving greater amounts of N fertilizer. However, since perennials have more root biomass , IWG will have greater NUE compared to wheat . MET HODS Site description The experiment was conducted at the W.K. Kellogg Biological Station (KBS) Long -term Ecological Research site , located in southwest Michigan, USA (42 o 24N, 85 o 24 W, elevation 288 m). The mean annual precipitation and temper ature are 1005 mm and 10.1 oC. KBS soils are in the Kalamazoo soil series (fine loamy) and Oshtemo (coarse loamy), mixed, mesic Typic Hapludalfs). These soils typically have an A horizon of 30 cm, a deep Bw/Bt horizon that reaches to 80+ cm, and a BC horizon to 140 cm. Prior to establishment in 2009, the field was under a corn ( Zea mays L.)-soybean [ Glycine max (L.) Meer.] -wheat ( Triticum aestivum) rotation. 15 Experimental design The study was established in 2009 as a split plot (3.1 m by 4.6 m) randomized complete block design experiment with four replicate blocks. The main factor is N treatments and the sub-factor is crop type . The N levels are an Low N ( Organic N) treatment, which received 90 kg N ha-1 of poultry manure; Mid N, which receiv ed 90 kg N ha -1 of urea; a nd High N, which received 135 kg N ha -1 of urea. N release from manure is typically slower than N release from urea and other inorganic fertilizers (Rees and Castle, 2002) such that N availability is ordered as Low N (Organic N) < Mid N< High N). The crop types assessed in this experiment were i) annual winter wheat var. Caledonia (soft wheat) and ii) Kernza TM (IWG) , which was developed through bulk breeding and mass selection at the Land Institute located in Salina, KS (DeHaan et al, 2004 ; Cox et al., 2010). Prior to planting , both plots were chisel plowed in September 2009. Every October, 2.24 Mg ha -1 of pelletized poul try manure and sawdust at 4 -3-2 N-P- Saranac, MI), was applied to the Low N ( Organic N) system. The application rate delivered 90 kg N ha -1 total N. The Mid N level is the recommended rate for conventionally grown wheat in the state of Michigan, while the High N (135 kg N ha -1) level received 50% more N than the Mid N level. Both the Mid N and High N syst ems received pelleted u rea at three different times throughout the growing season. In the conventional systems, a starter of 33 .6 kg N ha -1 and 53.8 kg K ha -1 as K2O for both Mid N and High N systems were applied immediately before planting. The following spring, plots were top -dressed with urea at 28 and 50.4 kg N ha -1 for Mid N and High N, respectively , typically at the beginnin g of April. In -depth details regarding timing of planting and chemical application can be found in Culman et al. (2013 ). 16 Abovegroun d biomass sampling Aboveground biomass was measured at grain maturity for both crops. In general, wheat was harvested in early to mid July and IWG was harvested at the end of July or early August. Aboveground biomass and yields were determined by randomly placing two 0.25 -m2 quadrats in every plot and clipping the crop biomass to 10 cm above the soil. Sampl es were then threshed to separate grain from straw and dried at 60 oC for 48 hours before weighing. Belowground biomass and soil sampling Belowground biomass and soil samples were collected near peak biomass and anthesis (mid June 2012) for both wheat and IWG . In 2011, belowground biomass was measured only in the Low N ( Organic N) and High N levels . A hydraulic direct -push soil sampler (Geoprobe, Salina, KS) was used to take three 1 -m cores per plot. The cores were 6 cm in diameter and subsequently divided into five depths (0 -10 cm, 10 -20 cm, 20 -40 cm, 40 -70 cm, and 70 -100 cm). The three cores were composited by depth interval and a subsample of 400 g from each depth was taken for root analyse s and the re mainder used for soil analyses. Soil moisture w as determined gravimetrically. Roots were separated into two size classes coarse (>6 mm) and fine (1 - 6 mm). Coarse roots were separated from soil by dry sieving through 6 mm sieves. Fine roots were obtained from soil sieved through 1 mm sieves by we t sieving. No attempt was made to determine live versus dead roots. To clean roots prior to weighing and drying, I soaked roots in deionized water and hand washed. Both coarse and fine roots were dried at 60oC for 48 hours prior to weighing. 17 Crop carbon ( C ) and nitrogen ( N) analysis Dried coarse and fine roots were frozen in liquid N and then immediately ground to a fine powder using a mortar and pestle. Dried grain and stem crop components were ground separately to 1 mm with a Wiley mill. Both above and belowground crop parts were analyzed for C and N in a CHNS analyzer (Costech Analyzer ECS 4010, Costech Analytical Technologies, Valencia, CA). In 2011, crop material was not analyzed for C and N. Nitrogen use e fficiency I used the mass balance approach to calculate NUE on the basis of fertilizer applied . Total Plant N (straw kg N ha-1 + grain kg N ha-1 + Coarse and Fine Root kg N ha-1)/ Total N applied (kg N ha-1); Aboveground N (straw kg N ha-1 + grain kg N ha-1)/ Total N applied (kg N ha-1); and Root NUE (Coarse and Fine Root kg N ha-1)/Total N applied (kg N ha-1). Ratios over 1.0 are an indication that the crop took up more N then was applied in that given growing season. Statistics All crop and soil responses were analyzed using Proc Mixed of SAS (version 9.3; SAS Institute, Cary, NC, USA). Crop, N level , and depth were treated as fixed effects and block as a random effect. Significant differences were determined at = 0.001 , 0.01, and 0.1. Double repeated measures were used to account for both depth and year in the model. Means were 18 RESULTS Weather and soil m oisture Cumulative precipitation and growing degre e-days between the months of March and October in 2011 and 2013 varied s ubstantially from 2012. KBS received above average precipitation during the 2011 and 2013 growing season s, receiving 858 mm and 752 mm respectively. Growing degree days (GDD) were sim ilar in 2011 and 2013 ( 2497 and 2435, respect ively), and were comparable to the 30 -year average (2431). In 2012, the Midwest experienced severe drought conditions from June to August. There were 276 and 337 more GDD in 2012 compared to 2011 and 2013; and 2 67 more than the 30 -year average. In 2012, KBS received a cumulative 557 mm of precipitation between March and October, which was substantially lower than the average (721 mm). There were no significant differences in gravimetric soil moisture across the N grad ient, and thus reported values are averaged across the Low N ( Organic N), Mid N, and High N systems. Gravimetric soil moisture was heavily influenced by crop, year, and depth (p=0.01, 0.0001, and 0.03, respectively). Although wheat consistently had greater gravimetric soil moisture than IWG , pairwise comparisons reveal that significant differences were mainly found at 40 -70 cm and 70 -100 cm (Table 2.1 ). In 2013 surface soil moisture (0 -10 cm, 10 -20 cm, 20 -40 cm) was 18% greater than in 2011 and 2012 for both crops (Table 2.1). Aboveground b iomass Aboveground biomass greatly differed between the two crops (Table 2.2, p=0.03). In 2011 and 2013, IWG had consistently greater aboveground biomass compared to wheat (Table 2 .2). In the 2012 drought year, both crops had lower productivity; averaging across N levels IWG biomass decreased by 69% and wheat decreased by 53% between 2011 and 2012. In 2013, IWG 19 aboveground biomass increased with greater levels of applied N; however, the N level e ffect was only marginally significant (p=0.08). Belowground b iomass IWG consistently had greater fine and coarse root biomass compared to wheat (Table 2. 2, p=0.0001). Depending on the N level and year, IWG had between 3 and 12 times greater coarse and fine root biomass than wheat . There was no overall N level effect on coarse root biomass (p=0.3). However, the significant crop by N level by year interaction (p=0.01) shows that IWG was more affected by N level than wheat , especially in 2013. For instance, pairwise comparison s reve aled that IWG coarse root biomass under High N was significantly greater than Mid N and Low N ( Organic N ) coarse root biomass (Table 2 .2, p=0.01, and p=0.002). There was a significant overall N level effect on fine r oot biomass (p<0.05). For IWG fine root biomass under Mid and High N were significantly greater than in the Low N ( Organic N) system (p<0.5). The crop by N level effect was significant (p=0.04) because increasing levels of N influenced only IWG . Crop allocation and root:shoot ratios Differences in crop biomass allocation were evident for wheat and IWG (Figure 2 .1). In non-drought years, IWG allocated between 23 and 50% of its total biomass to roots as compared to wheat , which allocated approx imately 10% to roots. In 2011 and 2013, IWG root:shoot ratios were two-times greater than wheat root:shoot ratio (Figure 2.1 , p<0.0001). The significantly high er root:shoot ratios evident in 2012 in comparison to 2011 and 2013 were caused by large reductio ns in aboveground biomass rather than gains in belo wground biomass. In 2012 IWG root 20 biomass was equal to 2011 and slig htly lower than 2013 (Table 2.2) . There were no significant N level effects on root:shoot ratios (Figure 2.1 , p=0.8). Coarse and fine ro ot biomass by depth IWG coarse root biomass was between 3.4 and 8 times greater than wheat coarse root biomass at surface depth intervals of 0 -10 cm, and 10 -20 cm (Figure 2.2, p<0.0001). There were a few marginally significant differences at mid (20-40 cm) and subsurface (40 -70 and 70 -100 cm) depth in tervals (p=0.05), but in general there were few differences between wheat and IWG coarse root biomass at lower depths (Figure 2 .2). Wheat vertical coarse root distribution was fairly consistent across the three years with 93% of t he roots found in the top 40 cm. IWG had similar vertical coarse root distribution s with 94% of roots typically found in the top 40 cm, however, IWG root biomass increased significant ly in the sur face depths over time (Figure 2.2). Despite the fact that there were no overall N level effects on coarse root biomass, there was a four -way interaction between year, crop, N level, and depth (p=0.01). Notable differences in IWG coarse root biomass across the different N levels at the surface depths likely caused this significant interaction. For example, pairwise compariso ns found that for 2011 and 2013 High N coarse root biomass at 0 -10 cm was significantly greater than Low N (O rganic N) coarse root biomass (P=0.0001 and P=0.000 1, respectively). In 2012, the Low N (O rganic N) system had greater coarse root biomass than Mid N (P<0.05), but was not significantly different from High N. There were no differences between N levels at subsurface depths. Greater amounts of variability occu rred at surface depths co mpared to subsurface depths, especially in 2012. Fine root biomass distributions were very similar to coarse root biomass distributions with the majority of roots concentrated in the top 20 cm. However, IWG allocated a great er amount of biomass to fine roots compared to c oarse roots below 40 cm (Figure 2.3 ). There were strong 21 differences in fine root biomass between crops (p<0.0001), across N levels (p=0.002), over time (0.0003), and by depth (P<0.0001). There was also a sign ificant three -way interaction among depth, year, and crop (p=0.03) , which reflected the variation in fine root production over time for IWG at surface depths compared to greater stability in wheat . Furthermore, significant differences in fine root production between IWG and wheat were typically only in the top 0 -40 cm. At the surface, IWG fine root biomass was typically between 1.5 and 4 times greater than wheat . IWG fine root biomass tended to in crease with increasing levels of fertilizer, especially at 0-10 cm, while wheat did not. IWG fine root biomass increased over time, with the greatest values occurring in 2013 under Mid N and High N systems. Crop N and nitrogen use e fficiency The to tal N contained in IWG coarse and fine roots consistently was greater compared to wheat (Table 2.3, P<0.0001). Aboveground biomass N content, however, was statistically similar between the two crops. Aboveground N content significantly differed across the N gradient, with greater N content typically found in the High N system (p=0.01) for both crops. There were significant pairwise comparisons across N levels for aboveground N in 2013 but not in 2012. The total N contained in c oarse root s generally increased with increasing N fertilizer additions (Table 2.3). The significant crop by N level interaction (p=0.004) along with pairwise comparisons indicate that N levels had a much stronger influence on IWG compared to wheat , especially in 2013. There was an overall N level effect on fine root N content (P=0.02). For IWG in 2012 and 2013, Mid N and High N had significantly greater fine root N compared to the Low N (Organic N) system (Table 2.3). Although there was an overall crop by N level interaction (p=0.2), wheat fine root N did not appear to be as strongly influenced by increasing N levels 22 compared to IWG . Total N strongly differed by crop (p<0.0001) and N level (0.0002), and was substantially greater in IWG and always larger in the High N level . There was a significant year effect for all crop parts, with N content typically greater in 2013. Coarse and Fine root N content were also examined by depth (data not shown) and exhibited very similar trends to coarse and fine root biomass by depth (F igure 2.2 and 2.3 ). The NUE for above and belowground biomass components were also calculated separately (Table 2.4). In terms of total crop NUE, IWG was more efficient at using N compared to wheat (p<0.0001). Across both years and N level, IWG NUE ratio s ranged from 0.8 to 1.5 and wheat NUE ranged from 0.56 to 0.86 (Table 2.4). IWG within the Mid N system exhibited greater NUE compared to Low N ( Organic N) and High N systems (p=0.03). The s ignificant interaction between crop and N level (P=0.03) is an indicator that N level had little effect on wheat NUE. Significant gains in NUE from 2012 to 2013 were visible in all three N levels for wheat and were most noticeable in the Mid N system for IWG . NUE increased by up to 53% in wheat from 2012 to 2013 by up to 43% in IWG (Table 2.4). There was no crop effect on aboveground NUE, as wheat and IWG were statistically similar to one another (p=0.9). H owever there was an overall N level effect, where wheat aboveground NUE was greater in Low N ( Organic N) systems and IWG aboveground NUE was greater under Mid N. Root NUE was substantially greater in IWG compared to wheat (p<0.0001). DISCUSSION OPT predicts that root biomass will decrease proportionately in systems receivi ng increased fertilizer or otherwise supplied with limiting nutrients at levels greater than crop need 23 (Bloom et al., 1985). In cases where nutrients are limiting, OPT predicts that plants will proportionally increase allocation to root growth. To accept OPT I would expect to find 1) a reduction in root biomass in systems receiving greater amounts of fertilizer and 2) increased root resource foraging under nutrient limited systems. I found neither expectation for either wheat or IWG , instead finding that annual root biomass remained stable across the N fertilizer gradient in all three years and root biomass of established IWG increased rather than decreased with greater amounts of N additions in 2013. Furthermore, there was no evidence for increased root foraging at depth in reduced N systems under either crop . OPT thus failed to adequately predict N level effects on root biomass and crop biomass allocation in situ . Nitrogen use efficiency on the other hand, was greater in IWG than in wheat, which is consistent with predictions that p erennial crops will use N more efficiently than annual systems (Jordan et al. 2007 ; Dawson et al. , 2008; Glover et al., 2010; Hirose, 2011 ). Root responses to added N The lack of a root response to increased N additions in IWG could be due to environmental and developmental factors. In 2011, IWG stands were two years old and still establishing, which could prevent observed responses to increased N fer tilizer (Jung and Lal, 2011). While I would then expect to see a root response to N level during the 3rd year, 2012 was a drought year, which apparently negated any response to N. H owever, I also failed to find a root response to N in 2013, when IWG was mature and growing conditions were favorable. In fact, in 2013 IWG root biomass significantly increased with higher levels of N, while root:shoot ratios remained stable, which is inconsistent with OPT. I also failed to find evidence for OPT in the wheat system , as root biomass remained stable across N levels. This could suggest that N was not the primary 24 limiting factor (Meinke et al., 1997) . Instead the wheat sy stem could have been limited by moisture, disease or another nutrient. In 2011, the Mid N, yields (63.7 ± 6.3 bu) were slightly lower than the wheat yield average at the regional level for southwest MI (69.5 bu, NASS, USDA). In subsequent years, the wheat yield dropped in 2012 (41.3 ± 8.3 bu) and 2013 (52.8 ± 6.9 bu). The lower yields in 2012 are the result of a drought year, however even in favorable growing condit ions evident in 2011 and 2013, the wheat yield tended to be lower than county averages, which could indicate that this system was limited by factors other than nitrogen. Another explanation for this lack of response to N could be the methodological a pproaches used in this study . For example, I sampled once during the growing season, perhaps at a time when nutrient resources were not limiting. However, I sampled near peak aboveground biomass, when crop nutrient demand is still high . The more likely dif ference between this study and research in support of OPT is that this study was conducted in situ, rather than in greenhouse pots or mesocosms (Davidson et al., 1969; Christie and Moorby, 19 75; Brewster et al., 1976). Growing conditions in greenhouses can be substantially different than field growing conditions and thus could influence root dynamics differently. That said, at least one in situ study has found support for OPT. For example, Jarchow et al. (2012) reported greater root biomass of C 4 grasses in unfertilized systems compared to unfertilized system. Interpretation is clouded by extreme nutrient limitati ons in the unfertilized system, which con trasts from this study, where each treatment received at least some N fertilizer. Heggensta ller et al. (2009) also report results for in situ study of switchgrass and big bluestem that are consistent with reported results. Increases in root diameter due to greater nutrient uptake in nutrient rich environments could explain greater root biomass i n systems receiving higher N additions (Ryser and Lambers, 1995). 25 OPT further predicts that when nutrients or water are limiting, crop allocation should shift to the production of fine roots that can capture resources available at greater depths (Bl oom et al., 1985). I found no evidence for enhanced fine root production in either wheat or IWG within the Low N (Organic N) level at any depth to 1 m. Likewise, Jarchow et al., (2012) , who found greater root biomass in an unfertilized C4 grass system at t he surface, also found no evidence of increased root production at depth, (although they did not distinguish between coarse and fine root biomass). These findings corroborate other calls for reconsideration of OPT ( Coleman and McConnaughey, 1995; Re ich, 2002; Janecek et al., 2014). OPT, may in fact be less useful for describing belowground resource allocation or simple developmental patterns compared to t he optimal foraging theory , where plants are expected to invest roots in highly enriched areas ve rsus more depauperate patches (Charnov, 1976; Loecke and Robertson, 2009; McNickle and Cahil, Jr., 2009). This seems consistent with results reported in this study, whereby roots increased under High N and were mainly concentrated in the top 0 -10 cm, rathe r than foraging deeper in the soil profile to obtain other available nutrients . Others have suggested that biomass partitioning is a function of ontogenetic drift, wherein biomass allocation is determined by growth and development rather than shifts in rea llocation due to limiting resources, as suggested by OPT ( Coleman and McConnaughay, 1995; Reich, 2002; Mcarthy and Enquist, 2007 ). The growth patterns of IWG in this study are consistent with this theory, as root biomass increased overtime, especially in the High N system. Perhaps in this system, the increased root biomass within the established IWG under High N is simply due to ch anges in development, allowing IWG to gain access to greater nutrient capture. 26 Nitrogen use efficiency in annual vs. perennial systems Above and belowground biomass responses to N additions can have profound impacts on internal and external crop N cycling. For this reason, I was also interested in determining whole -crop N use efficiency (NUE) . While there are many ways to define and calculate NUE (Dawson et al., 2008), in this study I consider whole -crop NUE to be total crop N (above +belowground biomass N) per N added , both in units of kg N/ha (Robertson and Vitousek, 2009). Th is mass balance approach allows us to determine the efficiency with which wheat and IWG assimilate added N. My hypothesi s that IWG would have greater whole -crop NUE compared t o wheat was supported, regardless of N level. Since aboveground NUE was not significantly different between wheat and IWG, it is likely that the extensive roots of IWG as well their large capacity for N storage are the main drivers for their high NUE values, which gives them an efficiency advantage over wheat . For example, in 2013, IWG root NUE increased by 40% in the Low N (O rganic N) and Mid N and by 87% in the High N treatments . Traditionally, root N content has not been includ ed in NUE calculation s (Weih, 2011). In three cases the IWG whole -plant NUE was greater than one, indicating that the crop took up more N than was applied, which demonstrates their ability to assimilate large amounts of N. NUE significantly differed across N level in IWG but not in wheat . IWG NUE was greatest in the Mid N level compared to the Low N ( Organic N) and High N levels. This does not support my hypothesis that NUE decreases with increasing levels of N. One explanation for greater NUE in the Mid N level could be that biomass production and N uptake kept up with N supply, compared to in the Low N ( Organic N) system, which always had lower above and belowground biomass. 27 Implications for enhanced ecosystem ser vices by perennial grain crop s These findings demonstrate that perennial grain cropping systems can significantly enhance ecosystem services in agriculture by increasing root biomass . Under a range of N additions, IWG produced up to 8 times more to tal root biomass than wheat in the top 40 cm of soil. No differences were found between the two crops deeper in the profile, refuting the hypothesis that perennial grain crops are likely have greater root biomass at depth compared to wheat (Cox et al., 200 6; Glover et al., 2010; Kell, 2011 ). Greater total root biomass in the perennial crop will likely lead to increases in soil organic matter, based on findings by others, who have found increased C sequestration under perennia l systems (Robertson, 20 00; West and Post, 2000; Syswerda, 2010 ). As perennial crops age, a greater standing stock of belowground biomass is established (Craine et al., 2003). This allows more C to accumulate in root biomass and soil due to root turnover, which provides between 3 0 and 80% of organic C inputs to soil (Kaly n and Van Rees, 2006). In this study, IWG root biomass increased by 51% from 2011 to 2013. While I did not measure total soil C, early results from this experiment show greater labile C under IWG compared to wheat in surface soil horizons (Culman et al., 2013). Increased root biomass has also been shown to enhance N cycling and accumulation (Fornara and Tilman, 2008). For example, increased root biomass in perennial systems c an lead to N immobilization , and the quick release of fine root N during turnover can lead to N retention and accrual (Fornara et al., 2009). These results demonstrate that increased root biomass enabled IWG to take up large a mounts of N and contributed to overall high NUE. As a resu lt, minimal N losses likely occur in these systems; for example, relative to wheat, IWG at this site reduced nitrate leaching by up to 99% in 2011 (Culman et al., 2013). 28 CONCLUSIONS In this study, e stablished IWG stands increase d root biomass with increasing levels of N fertilizer , while wheat root biomass remained stable despite varying levels of N. I found no evidence for increased root foraging at depth in reduc ed N systems under either crop. These results suggest that the opti mal foraging theory is a more adequate explanation for biomass allocation than OPT in these systems . Roots of IWG enhance N uptake and nitrogen use efficiency and appear to have contributed to the reduction of nitrate leaching . Given the C and N accrual an d the retention of N by their extensive root systems, perennial grain crops could contribute significantly to the environmental sustainability of agricultural systems. However their role in the longer -term sequestration of non -living organic carbon in soi ls remains uncertain. 29 APPENDIX 30 Table 2.1 Gravimetric soil moisture at five depths throughout the soil profile in wheat and IWG in 2011, 2012, and 2013, averaged across N levels (means ± se). Different superscript letters within years denote significant differences between crops for each depth and year combination at (p<0.05). 2011 2012 2013 Wheat IWG Wheat IWG Wheat IWG g kg -1 Depth cm 0-10 9.1 (0.6) a 8.0 (0.5) a 9.4 (0.6) a 8.4 (0.5) a 11.4 (0.5) a 10.8 (1.2) a 10-20 8.0 (0.5) a 7.3 (0.4) a 9.6 (0.5) a 8.0 (0.4) b 11.3 (0.5) a 10.2 (0.4) a 20-40 8.8 (0.6) a 8.5 (0.7) a 8.0 (0.4) a 7.3 (0.6) a 11.8 (0.6) a 10.6 (0.6) a 40-70 10.4 (0.5) a 8.5 (0.4) b 8.9 (0.5)a 7.7 (0.5) b 11.7 (0.6) a 9.5 (0.6) b 70-100 8.4 (0.6) a 7.6 (0.5) a 7.9 (0.6) a 5.6 (0.5) b 9.9 (0.9) a 7.8 (0.5) b 31 Table 2.2 Total biomass in wheat and IWG across three N levels in 2011, 2012, and 2013 (means ± se). Comparisons of cropping system means within a given year followed by same superscript letters are not significant. Different letters within a column of a given year denotes significant differences across N level. Coarse Roots Fine Roots Abovegro und Total Crop Biomass Wheat IWG Wheat IWG Wheat IWG Wheat IWG Mg ha -1 2011 Low N (Organic N) 1.1 (0.6) b 3.4 (0.5) a 0.31(0.02) a 0.99 (0.1) b 12.63 (1.8) c 14.9 (1.1) b 14.0 (2.2) b 17.34 (2.4) a Mid N NA NA NA NA 12.53 (1.2) c 19.91 (1.2) a NA NA High N 0.4 (0.1) b 5.0 (0.7) a 0.34 (0.05) a 0.99 (0.3) b 14.67b 15.94b 15.4 (0.8) b 21.89 (1.1) a 2012 Low N (Organic N) 0.7 (0.2) b 5.93 (0.5) a 0.26 (0.06) b 0.43 (0.07) a 5.99 (0.58) a 4.3 (0.6) b 7.02 (0.7) b 10.65 (0.8) a Mid N 0.9 (0.3)b 5.75 (0.5) a 0.24 (0.03) b 0.82 (0.2) a 5.16 (0.9) a 5.78 (0.9) a 6.25(0.8) b 12.36 (1.3) a High N 1.6 (0.4) b 5.8 (1.4) a 0.27 (0.03) b 0.78 (0.2) a 7.4 (0.8) a 5.33 (0.5) a 9.3 (0.3) b 11.9 (1.2) a 2013 Low N (Organic N) 1.1 (0.3) c 5.3 (0.7) b 0.21 (0.02) c 0.76 (0.07) b 8.4 (0.81) b 10.6 (0.7) a 9.77 (0.9) c 16.6 (0.6) b Mid N 0.8 (0.2) c 6.1 (1.0) b 0.33 (0.06) c 1.8 (0.33) a 9.68 (0.2) b 11.9 (0.8) a 10.8 (0.2) c 19.86 (1.5) a High N 0.8 (0.2) c 8.45 (0.6) a 0.43 (0.2) c 1.9 (0.4) a 8.99 (0.6) b 12.22a 10.2 (0.5)c 22.54 (1.4) a 32 Table 2.3 Total N Content in wheat and IWG across three nitrogen levels in 2012 and 2013 (means ± se). Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significant. Different letters within a column of a given year denotes significant differences across N level. Coarse Roots Fine Roots Aboveground Total Crop Biomass Wheat IWG Wheat IWG Wheat IWG Wheat IWG kg N ha -1 2012 Low N (Organic N) 5.84 (1.6) b 41.4 (6.1) a 1.5 (0.3) c 3.2 (0.3) b 53.4 (7.1) a 35.9 (3.5) a 60.9 (8.1) c 80.5 (9.4) b Mid N 6.86 (2.1) b 40.7(2.7) a 2.2 (0.3) c 5.24 (0.7) a 53.2 (14.2) a 49.2 (6.2) a 62.3 (10.1) c 95.1 (8.3) b High N 13.2 (4.1) b 55.3(8.5) a 2.5(0.2) c 5.5 (1.6) a 67.6 (10.3) a 49.8 (3.0) a 83.3 (4.8)b 110.6 (5.0) a 2013 Low N (Organic N) 6.9 (1.9) d 20.0 (2.9) c 2.0 (0.2) c 5.0 (0.5) b 58.2 (5.0) a 63.8 (5.4) ac 67 (5.8) d 88.8 (1.9) c Mid N 6.1(1.6) d 34 (6.0) c 3.3 (0.5) c 12.0 (2.4) a 68.1 (6.1) c 88.5 (10.7) b 77.5 (5.5) cd 134.5 (8.7)a High N 6.3 (1.6) d 52.1 (3.5) a 3.9 (1.6) c 13.5 (3.7) a 102.2 (15.8) a 76.2 (4.3) bc 112 (15.2) b 141.8 (9.9) a 33 Table 2.4 Nitrogen Use Efficiency in Harvested N, Root N, and Total Plant N. NUE was calculated as biomass N/total N applied. NUE ratios greater than 1.0 indicate that the crop took up more N than what was applied during the growing season. Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significant. Different letters within a c olumn of a given year denotes significant differences across N level. Aboveground NUE Root NUE Whole Plant NUE Wheat IWG Wheat IWG Wheat IWG 2012 Low N (Organic N) 0.52 (0.05) a 0.4 (0.04) a 0.07 (0.01) b 0.5 (0.07) a 0.59 (0.04) c 0.89 (0.1) b Mid N 0.46 (0.1) a 0.55 (0.07) a 0.1 (0.02) b 0.51 (0.02) a 0.56 (0.12) c 1.05 (0.09) a High N 0.45 (0.07) a 0.37 (0.03) a 0.12 (0.04) b 0.45 (0.06) a 0.57 (0.03 )c 0.83 (0.1) b 2013 Low N (Organic N) 0.65 (0.06) a 0.71 (0.06) a 0.09 (0.02) c 0.27 (0.05) b 0.75 (0.06) c 0.98 (0.02) b Mid N 0.76 (0.07) a 0.75 (0.06) a 0.1 (0.02) c 0.51 (0.09) a 0.86 (0.06) c 1.5 (0.09) a High N 0.75 (0.1) a 0.56 (0.03) a 0.08 (0.01) c 0.49 (0.04) a 0.83 (0.1) c 1.05 (0.07) b 34 Figure 2.1 Root:shoot ratios of wheat and IWG in 2011, 2012, and 2013 for over three N levels (Low N (Organic N), Mid N, and High N). The sum of total coarse and total fine r oots were used to calculate total root biomass. Total straw and grain were summed to determine total shoot biomass. 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Soil organic carbon sequestration rates by tillage and crop rotati nalysis, a global data. Soil Science Society of America Journal. 66:1930 1946. 42 CHAPTER 3: LITTLE EVIDENCE FOR EARLY SOIL CARBON CHANGE UNDER A PERENNIAL GRAIN CROP ABSTRACT Due to larger and more extensive root systems, perennial grain crops are expected to sequester carbon (C) and improve soil health. To examine the rate of soil C accumulation in a recently established perennial grain crop I compared C dynamics in perennial intermediate wheatgrass (IWG) against annual winter wheat (wheat) . I test ed whether or not different management practices influenced C dynamics under three available nitrogen levels, Low N (Organic N) system (90 kg N ha -1 poultry manure ), Mid N (90 kg N ha -1 urea) , and High N (135 kg N ha -1 urea ). I measured aboveground C (grain + straw ), and coarse and fine root C to a depth of one meter, and Particulate Organic Matter (POM), fractionated by size , was used to indicate labile and recalcitrant soil C pools. At harvest, IWG had 1.9 times more straw C and up to 15 times more root C compared to wheat. There were no significant differences in the large (6 mm-250 m) or medium (250 -53 m) POM-C between wheat and IWG (p>0.05) in surface horizons (0-10 cm). Large POM -C under IWG ranged from 3.6 ±0.3 to 4.0 ±0.7 g C kg soil -1 across the different levels of N, similar to wheat, whe re large POM -C ranged from 3.6 ±1.4 g C kg soil -1 to 4.7 ±0.7 g C kg soil -1 across N levels. Averaged across N level , medium POM -C was 11.3 ±0.7) g C kg soil -1 and 11.1 ±0.8 g C kg soil-1 for wheat and IWG , respectively. Despite larger pools of above and belowground C in IWG to 70 cm depth, I found no difference in labile or recalcitrant soil C pools between the two crops. Post -hoc power analysis revealed that in order to detect differences in the labile C pool at 0 -10 cm with an acceptable power (~80%), 52 replicates or a 43 15% difference in C between wheat and IWG were needed. I thus found no evidence for more soil C accumulation under IWG other than greater standing stocks of root C. INTRODUCTION Intensive agricultura l practices have depleted soil carbon (C) pools by up to 75% and contributed ~124 Pg C to the atmosphere over the past 140 years (Lal, 2011; Houghton and Hackler, 2001) . Several management practices can replenish the soil C pool (West and Post, 2002; Jarecki and Lal, 2010); one of the most effective is to conver t annual row crops to perennial vegetation (Post and Kwon, 2000; Syswerda et al., 2010; McLauchlan et al., 2006) . For example, Post and Kwon (2000) reported average C accumulation rates following conversion to grasslands of 33.2 g C m -2 y-1 and Gebhart et al. (1994) found rates as high as 110 g C m -2 y-1 12 years post conversion. Similar estimates have been reported for row crop conversion to forests although rates vary between tropical and temperate stands (Post et al., 2000). Evidence of C accrual in abandoned agricultural plots have also been reported with annual increases of 19.7 g C m-2 y-1 in surface soils (Knops and Tilman, 2000) Post and Kwon (20 00) explain that one of the most important drivers of C accumulation following conversion to perennials is an increase in soil organic matter inputs. Perennial systems often have between 3 and 10 times more belowground biomass compared to annual row crops (Culman et al., 2010; Zan et al., 2001; Dupont et al., 2014). Furthermore, C accrual will occur faster than respiration by heterotrophs in perennial vegetation because perennials are usually not tilled and are typically planted for longer intervals compare d to annual crops (Huggins et al., 1998). Other important factors that lead to C accumulation under perennials are the inputs of soil organic matter deeper in the soil profile and enhancing the physical protection of soil C through 44 aggregation (Six et al., 1998; Grandy and Robertson, 2006; Syswerda et al., 2010; Tiemann and Grandy, 2015 ). Less is known about the length of time required before increases in soil C are detectable or before soil C stabilizes post conversion (McLauchlan et al., 2006). In some cases, C accrual is detectable within the few years post conversion (Rehbein, 2015; McLauchlan et al., 2006) while in other cases detectable C accumulation can take over ten years (Syswerda et al., 2010). Much of this temporal variation reflects the rate of initial C accrual, which is largely dependent on the original C levels and how close a system is to reaching C equilibrium (Six et al., 2002). Further, soil C is comprised of different pools that turn over at different rates. A labile C pool with short turnover times can lead to only short-term C sequestration, whereas more recalcitrant C pools have longer residence times and consequently contribute to long -term C sequestration (Wander, 2004). The proport ion of C in the labile pools compared to recalcitrant pools is rarely determined and thus C stabilization potential post conversion is poorly understood. There is widespread interest to increase soil C in agricultural systems for both farm -scale and global benefits and one option could be to cultivate perennial grain crops in place of annual grain crops (Asbjornsen et al., 2013). Perennial wheat and perennial intermediate wheatgrass (IWG) are being developed to achieve the high yields of annual wheat and simultaneously have extensive root systems that could potentially increase soil C (Glover, 2010; Kell, 2011). For example, perennial grains developed by Dehaan et al. (2004) have significantly more coarse roots compared to wheat to 40 cm depth as well as more fine roots to 70 cm (Sprunger, Chapter 2). However, initial yields from perennial grain crops seem to peak after two or three years (Wagonner, 1990; Culman et al., unpublished), at which point a farmer would need to replant IWG or rotate to anothe r crop. Understanding whether initial gains in soil C can occur within this 45 period is important for determining the value of perennial grains as a plausible strategy for C sequestration. My objective here is to examine root C and labile and recalcitra nt pools of soil C under an experimental perennial grain crop (IWG) four years post conversion compared to annual winter wheat (wheat). I hypothesize d that 1) IWG will accumulate more C in labile and recalcitrant pools compared to wheat because of greater C inputs from both above and belowground sourc es; and 2) C pools will be greatest in systems receiving greater rates of N fertilization . METHODS Site d escription This study was conducted at the W.K. Kellogg Biological Station (KBS) , located in Southwest Michigan, USA ( 42o 24N, 85o 24 W, elevation 288 m). The mean annual precipitation and temperature are 1005 mm and 10.1 oC. KBS soils are in the Kalamazoo soil series (fine loamy) and Oshtemo (coarse loamy) , mixed, mesic Typic Hapludalfs . These soils typically have an A horizon to a depth of 30 cm, a deep Bw/Bt horizon that reaches 80+ cm, and a BC horizon that extends to 140 cm. Prior to the establishment of this experiment, this field was under a corn ( Zea mays L.) -soybean [ Glycine max (L.) Mee r.]-wheat ( Triticum aestivum ) rotation. Experimental design The experiment was established in 2009 as split plot randomized complete block design with four replicated blocks. The main factor was N level and the sub -factor was crop type equaling 24 plots (3 N levels by 2 crops by 4 blocks). Each plot was 3.05 by 4.57 m, with 2.43 m buffers in between the plots and 0.9 m buffers on the perimeter. The three N levels included 1) 46 Low N (Organic N), which received 90 kg N ha -1 of poultry manure; 2) Mid N, which received 90 kg N ha -1 of urea; a nd 3) High N, which received 135 kg N ha -1 of urea . Details on fertilization application and timing can be found in (Sprunger, Chapter 2). The two crops were i) annual winter wheat (Triticum aestivum L. var. Caledonia) and ii) IWG ( Thinopyrum intermedium (Host) Barworkth and D.R. Dewey ). IWG was developed through bulk breeding and mass selection at the Land Institute in Salina, KS (DeHaan et al, 2004; Cox et al., 2010). The Land Institute has trademarked this experimental grain as Kernza TM. Prior to establishment this site was chisel plowed to 20 cm and in subsequent years the wheat plots were rototilled to 15 cm depth. Prior to establishment in 2009, the field was under a corn (Zea mays L.)-soybean [ Glycine m ax (L.) Meer.] -wheat ( Triticum aestivum) rotation. Aboveground biomass sampling For this study, aboveground biomass was measured at maturity for both crops , where wheat was harvested on July 15, 2013, and the IWG was harvested on August 26, 2013. Aboveground biomass was determined by randomly placing two 0.25 -m2 quadrats in every plot and clipping the crop biomass to 10 cm above the soil. The aboveground biomass was separated into seed heads and straw. Then dried at 60oC for 48 hours and weighed. Seeds were separated from their hulls using a tabletop thresher. Belowground biomass and soil sampling Belowground biomass and soil samples were collected on June 7 th and 8 th, 2013, which was near peak aboveground biomass for both wheat and IWG. I used a hydraulic direct -push soil sampler (Geoprobe, Salina, KS) to extract three soil cores to 1 m depth, 6 cm in diameter from each plot. The three cores were subsequently divided into five depths (0 -10 cm, 10 -20 cm, 20 -40 47 cm, 40 -70 cm, and 70 -100 cm) and composited by depth interval. From each depth interval, a sub-sample of 400 g was taken for root analysis. Roots were separated in to two size classes , coarse (>6 mm) and fine (<1 mm). I obtained coarse roots by dry gently sieving soil sa mples through 6 mm sieves. I obtained fine roots by wet sieving the remaining soil through a 1 mm sieve. I made no attempt to separate live and dead roots. To ensure that roots were soil -free, I hand -washed roots by soaking them in deionized water. Both co arse and fine roots were dried at 60oC for 48 hours and then weighed. Crop C and N a nalysis Dried grain and stem s were ground separately to a fine powder. Dried roots were frozen in liquid N and then immediately ground to a fine pow der using a mortar and pestle. I analyzed both above and belowground crop parts for C and N in a CHNS analyzer ( Costech Analyzer ECS 4010, Costech Analytical Technologies, Valencia, CA). Crop C content was determined by multiplying crop biomass by C concentration . Labile and recalcitrant C pools I utilized physical size fractionation to determine particulate organic matter (POM), which has been shown to reflect both labile and more recalcitrant C pools (Cambardella and Elliot, 1992; Culman et al., 2012). Fir st, I gently sieved 100 g of soil through 6 mm as not to disturb soil aggregates (Ontl , 2013). Next, 10 g of air -dried soils and 30 mL of 0.05 sodium hexametaphospate were combined in 50 mL centrifuge tubes and placed on a shaker for 8 hours at 120 oscilla tions min -1. Using a water bottle filled with deionized water, I passed the solution of soil and sodium hexametaphospate through a 212 sieve (large POM), which was placed over a 0.053 mesh sieve (medium POM). The large POM fraction is associated with coarser 48 material and reflects the labile C pool, while the medium POM is comprised of silt and clay particles and is associated with more recalcitrant pools of C. The materials that were retained on both sieves were oven dried at 55¡ C until samples reac hed a constant weight. Dried samples were then ground using a mortar and pestle and analyzed for C and N as above. POM -C on an areal basis was determined by multiplying POM -C concentration, dry weight of POM -fraction, and length of depth interval. Statistics All above and belowground biomass as well as labile soil C data were analyzed separately using Proc Mixed of SAS (version 9.3; SAS Institute, Cary, NC, USA). Plant species, N level, and depth were treated as fixed effects and block as a ran dom effect. Significant differences were determined at = 0.05. For labile C and roots, depth was analyzed as a repeated measure. Means I used a post -hoc statistical power analysis to de termine if a type II error occurred during the POM -C statistical analysis. Power analyses have been widely used in soil science to determine if the lack of significance is more likely due to insufficient sampling (number of replications) or an absence of b iogeochemical differences between treatments (Kravchenko and Robertson, 2011; Ladoni et al., 2015). Detailed explanations of power analyses that have been used for soil C studies can be found in Garten and Wullschlegar (1999), Poussart and Olsson, (2004), and Kravchenko and Robertson (2011). In brief, I conducted a post -hoc power analysis that included 1) hypothesizing a size difference in the Large POM -C between wheat and IWG; 2) estimating the variability; 3) specifying a significance level of =0.05; 4) specifying the probability of detecting statistical differences (power); and 5) calculating a proposed number of replications. 49 The power analysis was conducted using the PROC MIXED procedure in SAS (version 9.3; SAS Institute, Cary, NC, USA). RESULTS Aboveground C Aboveground C significantly differed by crop type (Table 3.1 ). Grain C for wheat ranged from 1 .28 ± 0.14, s.e.m to 1.39 ± 0.15 Mg C ha -1 across N levels and was up to 25 times greater than for IWG, where grain C ranged from 0.07 ± 0.002 to 0.54 ± 0.1 Mg C ha -1. There was no overall N level effect as both crops had statistically similar grain C across N levels (F=5, p<0.6 ). Straw C was significantly greater in IWG compared to wheat (F=1.5, p<0.2). Averaging across N level s, IWG had 1 .9 times greater straw C compared to wheat . Both wheat and IGW aboveground C was similar across the three N levels. Root C and depth d istribution IWG coa rse root C was up to 15 times greater than that of wheat , where IWG coarse root C ranged from 1.70 ±0.30 to 2.42 ±0.13 Mg C ha -1 and wheat C mass ranged from 0.29 ±0.07 to 0.11 ±0.05 Mg C ha -1. Despite no overall N level effect (F=1.4, p=0.3), pairwise comparisons revealed that IWG coarse root C under high N was significantly greater th an coarse root C under Mid N and Low N (Organic N) (Table 3.2 , p<0.03). Wheat coarse root C was statistically similar across N levels. T he majority of root C was concentrated at the surface for both crops. Averaging across N level s, 60 % of IWG total root C was in the top 10 cm and 81% was in the top 20 cm. Wheat root C was even more concentrated at the surface , where , on average 79% of root C was in the top 0 -10 cm and 96% of root C was in the top 20 cm. IWG had significantly greater root C compared to whe at to 40 cm depths across all N levels (Figure 3.2 ). 50 Differences between wheat and IWG were also apparent for fine root C (Table 3.2), which was four times greater in IWG compared to wheat (F=34.6, p=0.0002). Total fine root C for IWG was 0.24 ±0.02, 0.47 ±0.09, and 0.47 ±0.10 Mg C ha -1 for Low N (Organic N) , Mid N and High N respectively. In cont rast, total fine root C was 0.063 ±0.001, 0.01 ±0.01, and 0.11 ±0.05 Mg C ha -1 for Low N (Organic N) , Mid N and High N respectively. There was a marginal overall N level effect on fine root C (F=3.0, p=0.1). In addition, pairwise comparisons showed that IWG under High N and Mid N had significantly greater root C content compared to IWG un der Low N (Organic N) (Table 3.2 , 0.03, 0.02, respecti vely ). IWG fine root C was more evenly distributed throughout the soil profile compared to coarse root C, but still a large portion was in the top 20 cm. For example, averaging across N levels, 48% of root C was in the top 10 cm and 72% was in the top 20 cm. Fine root distributions in the wheat systems mirrored the coarse root biomass distributions with 67% found in the top 10 cm and 92% found in the top 90 cm. IWG had significantly more fine root C compared to wheat to 70 cm depth in Mid N and High N levels (Figure 3.2). Differences between the two crops were only visible in the top 20 cm under Low N (Organic N). C and N concentrations and C:N ratios There was a significant crop effect for root C concentrations (F=98.9, p<0.0001), but differences between wheat and IWG mainly occurred in top 10 cm (Table 3.3 ), which explains the significant crop by N level by depth interaction (F=2.34, p=0.03). At the surface depth interval, IWG root C concentrations ranged from 28.9 to 33.1 % and were great er than in wheat, which ranged from 16.8% to 22.3%. Coarse root C did not significantly differ across N levels (F=3.1, F=0.07). Despite significant overall crop and N level effects on fine root C concentrations (F=0.05, p =0.01 and F=3.76, p =0.05), distin ct trends between the two crops for 51 fine root C concentrations were not as apparent compared to those in coarse roots. In general, greater C concentrations were found under the Low N (Organic N) level compared to the Mid N and High N levels (Table 3.3). Coarse root N concentrati ons were almost always greater in the wheat systems compared to IWG (Table. 3.4, F=77, p<0.0001 ) and decreased significantly by depth (F=26, p<0.0001) . There was also a strong N level effect, where coarse root N concentrations were typically greatest in the High N level (F=36, p<0.0001). There was a significant N level by crop by depth interaction (F=2.7, p=0.01), most likely caused by lack of differences across N level and between crops at depths below 40 cm. Fine root N conce ntrations differed by crop (F=75.7, p<0.0001) but not by N level (F=0.6, p<0.6). Wheat had greater N concentrations compared to IWG at almost every depth (Table 3.3). On average, fine root N concentrations were 36% greater than coarse root N concentrations for both crops. The C:N ratio for coarse roots was significantly greater in IWG systems compared to wheat at almost every depth (Figure 3.3, F=269, p<0.0001). There was also a strong overall N level effect (F=74.8, p<0.0001), where the coarse roo t C:N ratio was greater under Low N (Organic N ), especially at lower depths. Similarly, there was an overall crop (F=62.5, p<0.001) and N level (F=10.4, p<0.002) effect for fine root C:N ratio, where IWG had a significantly greater C:N ratio at all depths under Low N (Organic N) and greater C:N ratio in subsurface depths under Mid N and High N (Figure 3.4). In addition, there was a significant crop by N level interaction because IWG was more affected by N level compared to wheat (Figure 3.4). Particulate organic m atter C There were no significant differences in large or medium POM -C concentrations between the two crops (Figure 3.5, F=0.5 and p=0.5 and F= 0, p=0.9 , respectively ) or across N levels 52 (F=0.3, p=0.8 and F=0.6,p=0.9, respectively). The l arge POM -C concentrations were greatest in the top 0-10 cm of soil in both crops compared to other depth intervals. Mean IWG large POM -C concentrations at the surface depth were 3.6 ±0.4, 3.8 ±0.8, and 4.0 ±0.7 g C kg soil -1 for Low N (Organic N), Mid N, and High N respectively. Wheat POM C concentrations at 0 -10 cm depth ranged from 3.6 ±1.3 to 4.7 ±0.7 g C kg soil -1, with greater concentrations found in the Low N system. Medium POM -C was greater than the large POM -C. At 0 -10 cm, IWG medium POM -C ranged from 10.9 ±1.4 to 11.2 ±0.6 g C kg soil -1 across N levels, with the Mid N system having the lowest concentrations. Surface soil concentrations were very similar in wheat systems where concentrations ranged from 9.8 ±1.6 to 12.6 ±1.9 g C kg soil -1, again with concentrations slightly higher in the Low N (Organic N) system. Pairwise comparisons reveal that large POM -C concentrations below the 10 cm depth interval were statistically similar to one another (p>0.05). In contrast, m edium POM -C fractions significantly decreased by depth to 40 cm (p<0.0001). POM -C content accounts for the weight of the fraction, C concentration, and length of depth interval. There was no difference in large or medium POM -C content between the two crop s throughout the soil profile to 1 m (Figure 3.6, F=0 and p=0.9 and F= 0.11, p=0.7 , respectively ). Approximately 40% of POM -C was found in the top 0 -10 cm for both crops. POM -C below 20 cm was evenly distributed throughout the soil profile in the large frac tion, but steadily decreased by depth in the medium fraction (Figure 3.6) . In addition, POM -C content was statistically similar across N levels for both large and medium fractions (F=0.8 and p=0.5, F=1.6, p=0.2; respectfully ). 53 Power analysis I conducted a post -hoc power analysis for two different scenarios. First, I used the observed difference between wheat and IWG in the large POM -C at 0 -10 cm depth and simply increased the number of replicates. Second, I hypothesized a 15% difference in C between wheat and IWG, while keeping the number of replicates at n=4. The power values calculated for both scenarios are shown in figure 3.7 . For scenario one, a total of 52 replicates were needed in order to achieve 78% power. For scenario two, a 15% increase in the difference between wheat and IWG large POM -C with four replicates was needed to achieve 84% power. DISCUSSION Above and belowground C differed considerably between the annual wheat and perennial IWG systems, with root C contents up to 15 times greater in IWG. However, despite greater root C in the IWG system, I did not detect any differences in labile or recalcitrant soil C pools, as measured by POM -C between wheat and IWG four years after establishment. Crop C and POM -C fractions Peren nial crops are often touted for their greater and more extensive root systems compared to annual crops (Glover et al., 2007) , which was true for this study; I found that total coarse and fine root C stores of IWG were between 6 and 15 times gre ater than root C stores of wheat . The magnitude of difference s in root C between IWG and wheat is on par with other studies comparing annual and perenn ial crops (Jarchow et al., 2012; Anderson -Teixeira et al., 2013). My findings are also consistent with ex pectations that perennial grains will have greater root C at subsurface depths (Glover, 2010). Significant differences between the two crops were 54 detectable to 70 cm depth and demonstrate that perennials are capable of placing greater amounts of root C dee per in the soil profile compared to annual crops. Although the majority of aboveground biomass is removed in both IWG and wheat, there is a portion of aboveground C that is left on the soil surface. Given that IWG has significantly greater straw C than whe at, there also could potentially be more aboveground C contributing to soil C stores in IWG systems compared to wheat. However, despite greater overall aboveground C and up to 15 times more root C within the IWG systems compared to wheat , I did not find significant differences in the labile or recalcitrant soil C pool s between the two crops at any depth . Averaging across N level, mean concentrations at the 0 -10 cm depth for the large POM -C fraction was 4.1 g C kg soil -1 for wheat compared to 3.7 g C kg soil -1 for IWG (p=0.5). Surface mean m edium POM -C averaged across N level was 10.4 and 1 0.5 g C kg soil -1 for wheat and IWG , respectively (p=0.9) . These findings do not support the hypothesis that more labile and rec alcitrant soil C will accumu late under IWG compared to annual cereals. Furthermore, I found similar POM -C concentrations across the three N levels, even though IWG grown under High N had more root C than the IWG grown with Low N (Organic N) and Mid N levels. Given the widesp read evidence for gains in soil C under perennial systems compared to annual row -crops, it is surprising that I did not find greater soil C under IWG compared to wheat even after 4 years. Zan et al. (2001) found that willow stands used for biofuel producti on had 15% more soil C compared to corn after four years of production. In a review of soil C under biofuels, Anderson -Teixeira et al. (2009) consistently found that crops like switchgrass and miscanthus on average accumulated 1 Mg ha -1 yr-1 in the top 30 cm after 5 years. McLauchlin et 55 al. (2006) found a linear increase in labile and recalcitrant soil C in grassland systems that were between 0 and 40 years post conversion. Lack of increase in soil C under IWG One reason for the lack of increase in soil C here might be length of time since conversion and/or establishment. Forest and grassland systems that had increased soil C, reviewed by Post and Kwon (2001), were between 8 and 126 years post -conversion from cropland. In studies that reported soil C accumulation in perennial grasses or cellulosic biofuels compared to annual cropping systems, perennial systems were typically 4-15 years old (Syswerda et al., 2010; Collins et al., 2010; Rehbein et al., 2015). For example, at the nearby KBS LTER site, Sy swerda et al. (2010) found greater surface soil C concentrations 12 years post establishment in alfalfa compared to a conventionally managed corn -soybean -wheat system. Over a four -year period, Su (2007) detected C sequestration rates of 0.57 Mg C ha -1yr-1 following conversion to alfalfa. Rehbein et al. (2015) found a linear increase in soil C accumulation in both labile and recalcitrant pools in Miscanthus stands that ranged from 0-19 years post -conversion. In those stands, soil C accumulation in the coarse POM fraction accumulated within the first seven years and than reached saturation, while the silt and clay associated POM fractions continued to accumulate C over time. Nevertheless, in this study, I would expect to see an increase in at least the labile C pools after four years. The labile C pool is comprised of recent inputs from aboveground litter and or root rhizodeposition, and thus it is especially surprising that I did not detect an increase in soil C within the Large POM -C fraction. This may be because under IWG the labile C pool could be lower quality due to slower root decomposition compared to wheat. The C:N ratios of both 56 coarse and fine IWG roots were significantly greater than wheat throughout the entire profile, which could lead to red uced turnover and smaller C contributions within the initial years of establishment. A higher C:N ratio within perennial roots compared to annual roots is common (Craine et al., 2003) and the greater C content could lead to longer root persistence. Over t ime, as the roots higher in C content turn over, gains in soil C might be detected under IWG. Another plausible explanation for the lack of differences in soil C between the two crops is priming under IWG . The priming effect occurs when increased r oot exudates stimulate microbial activity , causing an increase in decomposition rates of older soil C (Cheng, 1999). Strickland et al. (2015) found a 21% decline in total soil C in established switchgrass stands mainly due to losses in POM -C. They attribut ed this loss of C to priming that occurred due to increased microbial activity. In the present study, omnivore nematodes were greater under IWG (Culman et al., unpublished), which could have led to increased decomposition. A final explanation for a f ailure to detect differences in soil C under this IWG system could be due to limitations in methodology. Although the POM fractionation procedure has been widely used to detect system level differences in soil C in both labile and recalcitrant pools (Camba rdella and Elliot, 1992; Rehbein et al., 2015), POM -C still reflects more of a recalcitrant or processed C compared to other methods like microbial biomass and permanganate oxidizable C (C ulman et al., 2012). Sprunger (C hapter 4) found that long -term incub ations that utilize the degradation of enzymes to determine soil respiration were more effective at detecting C dynamics across annual and perennial cropping systems compared to POM fractionation. However, C mineralization results from this same site show no difference between IWG and wheat four years after establishment (Culman et al., unpublished). 57 A power analysis further helps to explain the lack of significant soil C differences considering the Large POM -C at the 0 -10 cm depth interval, which is wher e I most expected to see a difference between the two systems. The analysis revealed that 52 replicates would likely be needed to reach an acceptable probability (78%) of detecting a small significant difference (p=0.05) in large POM -C at that time. With f our replicates in this study, alternatively, a 15% difference in surface soil C between wheat and IWG would be needed to achieve 84% power. Over time, then, soil C might accumulate sufficiently to reveal a 15% difference in POM -C. However, a long -term expe riment would be required to capture such differences and in any case would take longer than the expected 3 year perennial grain rotation now projected (Wagonner, 1990; Culman et al., unpublished). This power analysis thus reinforces the fact that more time is needed in order to detect difference in C between wheat and IWG. Vision of perennial grains as a tool for soil C accumulation The concept of perennial grains as a means to increase yields while providing ecosystem services within agricultural landscapes has garnered much attention (Wagoner, 1990; Glover, 2007). In particular, proponents of perennial wheat development argue that a perennial version of wheat could lead to crops that are more productive with less need for fertilizers, that amelio rate erosion and reduce nitrate leaching, and that possess greater water use efficiency (Glover et al., 2010; Kell, 2011; Culman, 2013). Proponents especially tout the potential for soil C accrual throughout the soil profile due to dee p roots (Crews and De Haan, 2015; Asbjornsen et al., 2013). Critics dispute the claim that perennial wheat and IWG will be viable from a production standpoint and argue that increasing seed production while maintaining characteristics of a perennial system is insurmountable wi th current breeding efforts (Smaje, 2015). 58 Perennial wheat and IWG yields at KBS are 50% and 70% lower than annual winter wheat yields (Jaikumar et al . 2012; Culman et al., 2013). H owever , proponents argue that it could still be valuable to farmers who want to improve soil health and other ecosystem services (Adebiyi et al., 2015). Four years post establishment, I was unable to detect any gains in C accumulation under IWG compared to wheat in either labile or recalcitrant pools. Four years may be an insufficient amount of time to detect gains in C under IWG and given the large amount of belowground C content, soil C gains could eventually occur. However, because yields decline after three or four years, gains in C that fail to show up in this time per iod may never be realized before farmers rotate to another crop. Soil C sequestration is not the only ecosystem service that IWG can provide. For example, Culman et al. (2013) found that IWG reduced nitrate leaching to up to 99% c ompared to wheat and Sprunger (C hapter 2) found that IWG improved crop -level N use efficiency by up to 42%. In addition, there is evidence that perennial roots persist even after a new annual crop is established (Dupont et al., 2014). Perennial roots could , therefore, contribute to soil C pools after conversion to an annual system. Nevertheless, the undetectable soil C accumulation within a short time period weakens the appeal of perennial wheat and IWG. CONCLUSIONS I measured labile an d recalcitrant soil C pools in wheat and 4 th year IWG across three N levels differing in rates and types of N . Co arse and fine root C were up to 15 times greater under IWG compared to wheat. However, I did not detect any soil C gains under IWG in either labile or recalcitrant pools. Due to the larg e C stores found in above and b elowground biomass in IWG systems, it is reasonable to expect gains in soil C over time. However, the post -hoc power 59 analysis reveals that detecting a significant difference in C would require either a large num ber of replicate samples or a greater (15%) difference between wheat and IWG. Since yields of perennial IWG decline after two or three years, the insignifican t soil C accumulation after four years weakens the a ppeal of perennia l grain crops. 60 APPENDIX 61 Table 3.1 Grain and straw between wheat and IWG across three N levels (Low N (Organic N), Mid N, and High N). Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significantly different. Different lower case letters denote significan t differences between crops and across N levels. Grain Straw Wheat IWG Wheat IWG Mg C ha -1 Low N (Organic) 1.28 (0.14) a 0.07 (0.002) b 2.34 (0.28) b 4.59 (0.41) a Mid N 1.43 (191) a 0.11 (0.02) b 2.73 (0.24) b 5.190 (0.40) a High N 1.39 (0.15) a 0.54 (0.01) b 2.42 (0.18) b 5.36 (0.25) a 62 Table 3.2 Coarse and fine root C contents between wheat and IWG across three N levels (Low N (Organic N), Mid N, and High N). Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significantly different. Different lowe r case letters denote significant differences between crops and across N levels. Coarse Roots Fine Roots Wheat IWG Wheat IWG Mg C ha -1 Low N (Organic) 0.29 (0.07) c 1.74 (0.29) b 0.063 (0.001) c 0.24 (0.02) b!Mid N 0.19 (0.06) c 2.42 (0.13) a 0.10 (0.01) c 0.47 (0.09) a!!High N 0.16 (0.03) c 2.42 (0.13) a 0.11 (0.05) c 0.47 (0.1) a! 63 Table 3.3 Carbon concentrations for coarse fine root biomass across N levels (Low N (Organic N), Mid N, and High N) at five depths. Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significant. Different lower case letters denote significant differences between wheat and IWG. Coarse Root C concentration Fine Root C concentration Management Soil depth Wheat IWG Wheat IWG cm g C kg -1 g C kg -1 Low N (Organic N) 0-10 22.3 (0.9) a 31.1 (2.8) b 29.8 (1.5) a 28.7 (2.2) a 10-20 27.6 (1.4) a 31.8 (0.4) a 29.1 (0.9) a 29.9 (1.7) a 20-40 30.3 (0.8) a 36.1 (1.2) a 30.3 (2.5) a 35.5 (0.3) a 40-70 21.9 (7.4) a 38.4 (0.6) b 30.8 (1.9) a 33.6 (1.1) a 70-100 No Roots 28.7 (4.3) * 26.5 (4.9) a 28.8 (1.3) a Mid N 0-10 16.8 (1.2) a 29.8 (2.8) b 28.9 (3.9) a 21.8 (3.2) b 10-20 25.4 (2.1) a 30.9 (5.10) b 30.2 (1.2) a 17.4 (3) b 20-40 24.2 (2.4) a 25.64 (2.4) a 29.4 (1.2) a 26.5 (3.2) a 40-70 27.9 (5.6) a 29.7 (0.6) a 31.1 (1.0) a 32.7 (0.4) a 70-100 No Roots 28.4 (1.4) * 32.8 (5.0) a 30.1 (1.1) a High N 0-10 18.5 (2.5) a 33.3 (1.9) b 31.1 (1.6) a 16.9 (0.8) b 10-20 21.9 (1.3) a 26.8 (3.3) a 24.4 (3.3) a 17.9 (1.6) b 20-40 24.8 (2.7) a 33.7 (3) a 31.1 (2.3) a 29.1 (3.4) a 40-70 20.1 (0.6) a 27.5 (2.8) a 28.9 (2.2) a 32.4 (1.1) a 70-100 12.9 (0.8) a 22.6 (4.9) a 34.0 (1.7) a 30.4 (2.3) a 64 Table 3.4 Nitrogen concentrations for coarse fine root biomass across N levels (Low N (Organic N), Mid N, and High N) at five depths. Comparisons of means within rows (among cropping system) followed by same lowercase letters are not significant. Different lower ca se letters denote significant differences between wheat and IWG. Coarse Root N Concentrations Fine Root N Concentrations Management Soil depth Wheat IWG Wheat IWG cm g N kg -1 g N kg -1 Low N (Organic N) 0-10 0.89 (0.06) a 0.57 (0.03) b 1.3 (0.08) a 0.86 (0.09) b 10-20 0.48 (0.1) a 0.43 (0.01) a 0.98 (0.02) a 0.77 (0.03) b 20-40 0.59 (0.06) a 0.30 (0.03) a 0.95 (0.06) a 0.65 (0.06) b 40-70 0.44 (0.05) a 0.26 (0.04) a 0.74 (0.09) a 0.49 (0.02) b 70-100 No roots 0.33 (0.06) * 0.66 (0.08) a 0.55 (0.06) a Mid N 0-10 0.8 (0.04) a 0.8 (0.02) a 0.99 (0.2) a 0.68 (0.1) b 10-20 0.86 (0.04) a 0.62 (0.07) b 1.1 (0.05) a 0.64 (0.09) b 20-40 0.7 (0.04) a 0.44 (0.02) a 1.0 (0.04) a 0.6 (0.04) b 40-70 0.77 (0.06) a 0.46 (0.04) b 0.89 (0.08) a 0.66 (0.03) b 70-100 No roots 0.48 (0.03) * 0.92 (0.09) a 0.64 (0.03) b High N 0-10 0.85 (0.06) a 0.93 (0.01) a 1.09 (0.2) a 0.8 (0.02) b 10-20 0.84 (0.07) a 0.68 (0.04) a 1.0 (0.09) a 0.59 (0.1) b 20-40 0.81 (0.05) a 0.52 (0.05) a 1.1 (0.09) a 0.7 (0.06) b 40-70 0.77 (0.05) a 0.54 (0.09) a 0.9 (0.05) a 0.76 (0.1) a 70-100 0.38 (0.04) a 0.41 (0.04) a 0.84 (0.2) a 0.67 (0.06) a 65 Figure 3.1 Coarse root C content for annual winter wheat (triangle s) and IWG (circle s) for three N levels ( Low N (Organic N), Mid N and High N) at five different soil depths. Error bars represent the standard error o f the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. 66 Figure 3.2 Fine root C in annual winter wheat (triangle s) and IWG (circle s) for three N levels ( Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error o f the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. 67 Figure 3.3 Coarse root C :N ratios for annual winter wheat (triangle s) and IWG (circle s) for three N levels (Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error of the mean and asterisks denotes significance at <0.05 and t denotes significance at <0.1. 68 Figure 3.4 Fine root C :N ratios for annual winter wheat (triangle s) and IWG (circle s) for three N levels ( Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error o f the mean and asterisks denote significa nce at p<0.05 and t denotes significance at p<0.1. 69 Figure 3.5 Large and Medium POM -C concentrations for annual winter wheat (triangle s) and IWG (circle s) for three N levels (Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error of the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. 70 Figure 3.6 Large and Medium POM -C content for annual winter wheat (triangle s) and IWG (circle s) for three N levels ( Low N (Organic N), Mid N and High N) at five different depths throughout the soil profile. Error bars represent the standard error of the mean and asterisks denote significance at p<0.05 and t denotes significance at p<0.1. 71 Figure 3.7 Probability (power) of detecting a statistical ly significant difference at (p=0.05) in surface (0 -10 cm) POM -C between wheat and IWG for two scenarios: 1) increasing the number of replicates but keeping the observed difference in POM -C between wheat and IWG, and 2) keeping the same number o f replicates (n=4) but using a hypothesized increase in POM -C (15 %). 72 REFERENCES 73 REFERENCES Adebiyi, J., L. Schmitt Olabisi, and S. Snapp. 2015. 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Zan, C. S., J. W. Fyles, P. Girouard, and R. A ., Samson. 2001. Carbon sequestration in perennial bioenergy , annual corn and uncultivated systems in southern Quebec. Agriculture, Ecosystems & Environment . 86:135144. 77 CHAPTER 4: CHANGES IN ACTIVE AND SLOW SOIL CARBON POOLS UNDER PERENNIAL BIOENERGY CROPS IN CONTRASTING SOILS ABSTRACT Differences in soil carbon (C) accumulation rates can markedly affect the sustainability of ecosystems managed for food and fuel production. I examined soil C accumulation and persistence in candidate biofuel cropping systems that differed in life hi stories (annual vs. perennial) and diversity (monoculture vs. polyculture) five years post -establishment, in ten replicated systems at both a moderate and high fertility site . I measured active, slow, and resistant C pools via long -term laboratory incubati ons and acid hydrolysis extraction. Cropping systems included four annual systems (no -till continuous corn and each phase of a corn -soybean -canola rotation), three monoculture perennial systems (switchgrass (Panicum virgatum) , miscanthus ( Miscanthus giganteus), and hybrid poplar ( Populus nigra P. maximowiczii )), and three diverse herbaceous perennial systems (a five -species native grass assemblage, an early successional community, and a restored prairie). Replicate systems were sampled at both a moderate fertility site in southwest Michigan (Kellogg Biological Station; KBS) and a high fertility site in south central Wisconsin (Arlington; ARL). Surface (0 -10 cm) cumulative C mineralization was greatest in the diverse cropping systems at both sites; at KBS, the native grasses (65 g C g -1 soil-1) and early successional C g -1 soil-1) had significantly greater C mineralization fluxes compared to all the other systems (p<0.05). I found substantial differences in active C betw een the annual monoculture and the perennial polyculture crops but not between the annual and perennial monoculture crops. Active C pools under perennial polycultures were over 2.5 times greater than under continuous corn, 78 and among systems followed the ra nk order continuous corn (237 g C g -1 soil) << early monocultures, only the poplar system had 2.5 times more active C than the annual systems. System differences in the slow C pool were less apparent, and there were no significant differences among the systems in the resistant C pool. At ARL, the more fertile site, the restored prairie system (75 g C g -1 soil-1) had significantly greater cumulative C mineralization than all other systems (p<0.05). Active C pools were similar to those at KBS, however, differences amongst systems were insignificant five years post -establishment, except the restored prairie and rotational corn had 3.4 times more active C than other syst ems. ARL accumulated significantly greater C in the resistant pool compared to KBS at every depth except 50 -100 cm. Patterns of particulate organic matter carbon (POM-C) among systems were not consistent with long -term incubation results. These findings d emonstrate that poplars and diverse perennial bioenergy systems are more effective at increasing C in the active pool than no-till annual crops and monoculture perennials, es pecially in less fertile soils. The fact that I did not find any differences in C accrual between monoculture perennials and no -till annuals suggests that no -till management may be equally advantageous to perenniality. Overall, these findings demonstrate that diverse perennial biofuels grown on marginal lands could lead to signific ant and rapid increases in C accumulation. INTRODUCTION Soil carbon (C) plays an important role at both local and global scales. At the local scale, C is crucial for improving soil structure, increasing biological activity, and increasing nutrient and 79 water availability , all of which lead to healthier and more productive so ils (Lal et al., 2011; Seremesic et al., 2011). Globally, soil C has an important role in balancing the C cycle as soil C can ser ve as a source or a sink of atmospheric carbon dio xide (CO 2). When photosynthetic inputs exceed decomposition rates, soil C accumulates. However, when soil heterotrophs respire C at a faster rate than C accumulates, soil C is lost to the atmosphere as CO2. Since soils hold twice the amount of C globally c ompared to the atmosphere (Swift, 2001), the stabilization of soil C pools is crucial for regulating atmospheric CO 2 concentrations. Efforts to sequester soil C are motivated by the fact that anthropogenic activities have led to soil C losses of up to 100 Pg worldwide (Paustian, 2002). Replenishing the soil C pool could lead to atmospheric CO 2 mitigation as more C would be sequestered in the soil versus released to the atmosphere. Sequestering C in agricultural landscapes is especially attractive as it can enhance other ecosystem services like soil health and crop yields (Lal, 2011). Well -studied approaches to enhancing soil C consist of either slowing decomposition by converting to no -till management or increasing C inputs by adding manure, cover cro ps, or additional crop residue (Hutchinson et al., 2007; Jarecki and Lal, 2010; Johnston et al., 2009). Planting perennial vegetation in place of annual crops is another strategy that could increase soil C by decreasing decomposition while simultaneo usly increasing organic matter inputs (Kell, 2011). Perennial species typically exhibit extensive root systems that can contribute large amounts of C belowground (Dupont et al. , 2014). Furthermore, perennial crops are no -till by nature and thus decompositi on is slowed as compared to annually tilled systems . Sperow et al. (2003) estimated that conversion from annual row crops to perennial vegetation could sequester 28 Tg C yr -1. Yet another strategy for increasing soil C is enhancing plant diversity (Steinbeiss et al., 2008) . For example, Fornara and Tilman (2008) found that grassland communities with 80 increased diversity sequestered 5 times more C compared to monoculture systems of the same species. While conversion to perennial vegetation has proven to be effective at increasing total SOC, the proportion of C accruing in labile versus more recalcitrant pools is poorly known. The labile or active pool consists of freshly deposited material such as plant residue and root exudates, and typically h as a mean residence time (MRT) of less than a year. In contrast, the slow pool is comprised of material that has been stabilized through physical and biochemical processes and has a MRT that ranges from a few years up to a decade, and the passive or resist ant pool consists of non -hydrolyzable C that is closely associated with the inorganic fraction of soil and has a MRT of thousands of years (Paul et al., 2001; Wander, 2004). Several techniques have been used to separate and quantify pools of soil C including biological, chemical, and physical methods . Previous research has shown that biological approaches via long -term incubations are particularly informative for discerning C accumulation in active and slow pools post disturbance or land conversion (Pa ul et al. 1999; 2001). For example, Paul et al. (1999) found that early successional systems accu mulated more soil C in the slow pool compared to conventionally tilled annual crops, and overall, poplars were the most effective at stabilizing C. Collins et al. (2010) found that the slow C pool under five year -old switchgrass stands was 13% greater than in the nearby uncultivated native soils. Physical techniques, such as p articulate organic matter (POM) size and density fractionation, can be used to isolate different physical fractions of soil C (Cambardella and Elliott; 2000). The lighter and large r POM fractions are typically more associated with labile pools of C compared to the more processed , heavier and smaller fractions, which represent a more stabiliz ed pool of C (Six et al., 2000; Wander, 2004; Culman et al., 2012). 81 Here I utilize long -term incubations and POM fractionation to investigate C pools across eight potential biofuel cropping systems ranging in perenniality and diversity from continuou s corn to restored prairie , and comparing equivalent cropping systems in two soils contrasting in fertility. I hypothesize d that 1) perennial systems will have greater active and slow C pools compared to annual systems due to their more persistent roots, w hich contribute large amounts of C belowground; 2) in general, active C pools will accumulate faster at a more fertile site (Arlington; ARL) compared to a less fertile site (Kellogg Biological Station; KBS), because of higher C stocks and higher clay conte nt; and 3) perennial systems higher in diversity will have more root production and thus greater soil C accumulation. METHODS Site description Hypotheses were tested in the Biofuel Cropping System Experiments (BCSE) located at Arlington Agricultural Research Station (ARL) in Wisconsin, USA and the Kellogg Biological Station (KBS) Long -Term Ecological Research Site located in Michigan, USA. ARL has more fertile soils than KBS . Both sites are part of the U.S. Department of Energy Great Lakes Bioenergy Research Center (GLBRC). Mean annual precipitation and temperature at ARL are 833 mm yr -1 and 7.4 oC, respectively. Soils are silt y loam mesic Typic Argiudolls in the Plano Series (Sanford et al., 2012), with five horizons: Ap (0 -23 cm), A (23 -36 cm), Bt1 (36 -48 cm), Bt2 (48 -79cm), and Bt3 (79 -109 cm). Prior to establishment in 2008 the surface (0 -10 cm) pH was 6.6, total soil C was 22.4 g C kg -1 (Sanford et al., in press), and soil texture was 9% sand, 66% silt, and 25 % clay ( http://data.sustainability.glbrc.org ). Mean annual precipitation and temperature at KBS are 1005 mm yr -1 and 10.1 oC. Soils at KBS are well -drained loam y mesic 82 Typic Hapludalfs and primarily within the Kalamazoo and Oshtemo series with five distinct horizons: Ap (0 -30 cm), E (30 -41 cm), Bt1 (41 -69 cm), 2 Bt2 (69 -88 cm), 2E/Bt (88 -152) (Robertson and Hamilton, 2015). In 20 08 surface soils at KBS (0 -10 cm) had a pH of 6.1, total soil carbon was 14.3 g C kg -1, and texture was 63% sand, 31% silt, 6% clay (http://data.sustainability.glbrc.org ). Prior to experiment establishme nt in 2008 both sites were under annual row crops. Experimental design and systems The BCSE is a randomized complete block design at each site with five replicate blocks consisting of nine biofuel cropping systems that include annual row crops, mo noculture perennial crops, and diverse herbaceous perennial crops (Table 1). Four annual row crops consist of continuous corn (Zea mays L.) and each phase of a corn -soybean ( Glycine max L.)- canola (Brassica napus L.) rotation. The perennial systems includ e three monocultures and three mixed plant assemblages. The perennial monoculture systems are switchgrass (Panicum virgatum L.), miscanthus (Miscanthus ! giganteus) and hybrid poplars ( Populus nigra ! P. maximowiczii onsist of a five species native grass mix ( Andropogon gerardii, Elymus canadensis, Panicum virgatum, Schizachrium scoparium, and Sorghastrum nutans) , an early successional community, and an 18 -species restored prairie consisting of C3, C4, and legume speci es. Prior to planting, all plots were tilled with a chisel plow and secondary soil finisher in early spring 2008. The annual row crops were subsequently planted in late spring, and thereafter treated as no -till. Planting rates for corn and soybeans were 70,000 and 78,000 seeds ha -1, respectively. Canola was planted at 4.5 kg ha -1. The switchgrass, native grasses, and restored prairie systems were planted in the summer of 2008 with a brillion -type native plant seeder. 83 Seeding rates for switchgrass w ere 7.5 kg ha -1. Planting densities for the native grasses ranged from 1.6 to 2.4 kg ha -1 and restored prairie planting densities ranged from 0.4 to 1.2 kg ha -1. Both the miscanthus and the poplar systems were planted by hand in May 2008 at densities of 17,200 rhizomes ha -1 and 2,778 cuttings ha -1, respectively. Miscanthus failed at ARL due to winterkill in 2009 and was replanted in spring 2010 (Sanford et al. in press). The early successional system reflects natural succession post cessation of agric ulture and its composition reflects the soil seed bank and natural colonization. Each plot within the BCSE is 27 m x 43 m (0.12 ha) and plots are separated by a 15 m - wide mowed alley. Nitrogen fertilizer application varied by cropping system. All c orn systems received on average 167 kg N ha -1 y-1 as urea -ammonium nitrate at both ARL and KBS . Canola systems received 176 kg N ha -1 y-1 as urea -ammonium nitrate. The switchgrass, miscanthus, native grasses, and early successional systems each received 56 kg N ha -1 y-1 of ammonium nitrate. The poplars received a single pulse of ammonium nitrate fertilizer in 2010 at a rate of 155 kg N ha -1 at KBS and 210 kg N ha -1 at Arlington. The restored prairie and soybean systems were unfertilized. Soil sampling Intact soil cores were collected in November 2013 at both sites from blocks 1 -3 with hydraulic direct -push soil sampler s (Geoprobe; Salina, KS at KBS and Giddings ; Windsor, CO at ARL ). Three 100 -cm deep cores (7.6 cm diameter) were taken at three designa ted sampling stations within each plot and divided into four different depths: 0 -10 cm, 10 -25 cm, 25 -50 cm, and 50 -100 cm. Cores within each plot were composited by depth interval and sieved to 4 mm. 84 Long -term incubations Long -term laboratory incu bations were used to estimate the turnover rates of the different soil organic C pools. The laboratory experiment was a two -site by ten -cropping -system full factorial design. Surface soils (0 -10 cm) were analyzed from all systems and subsurface soils (10-25 cm, 25 -50 cm, and 50 -100 cm) were analyzed in the corn, switchgrass, native grasses, and restored prairie systems. Two analytical replicates were treated as subsamples. Twenty -five grams of fresh soil were placed in 237 mL glass Mason jars. I adjust ed soils to 55% water -filled pore-space utilizing the methods described in Franzluebbers et al. (2000). I carried out a pilot study wherein surface soils from the continuous corn plots were incubated to determine optimal C mineralization rate over a range of water contents. Throughout the experiment soils were kept in the dark at 25 oC. Soil moisture was adjusted once per week to maintain moisture between 45 -55% throughout the course of the incubations. CO 2 measurements were taken 11 times over the course of 322 days, with more intensive sampling at the beginning (once per week) and less towards the end (once every 6 weeks). CO 2 production of each sample was determined by injecting 1 mL of headspace into a N 2 carrier gas that streamed through a LI -COR LI -820 infrared gas absorption analyzer (LI -COR Biosciences, Lincoln, NE). An initial CO 2 reading was taken immediately after jars were capped, followed by three subsequent readings separated by 40 minutes. CO 2 fluxes were calculated by regressing CO 2 respiration versus time (Robertson et al., 1999). Acid hydrolysis To determine the resistant or non -hydroly zable C pool, I performed acid hydrolysis on soils after the last incubation (Paul et al., 1999; Collins et al., 2000; Sanford and Kucharik, 85 2013). Prior to hydrolysis, I used a dissecting scope (20x) to identify plant material from previously sieved (4 mm) soil . Next, I removed any plant material by hand and by flotation using a 5% NaCl solution. Two grams of soils were refluxed in 6N HCl (20 mL) at 116 oC for 16 hours. This process causes available C to be released as CO 2 and amino compounds, pectins, and cellulose to solubilize (Sollins et al., 1999). The remaining material is closely related to the parent material and was washed by centrifugat ion, dried, and ground for total C and N analysis with a CHNS Elemental Analyzer (Costech ECS 4010, Costech Analytical Technologies, Valencia, CA) . The three pool model In order to determine active, slow, and passive pools I combined results from non-linear regression and acid hydrolysis and used a three -pool model with first -order kinetics: Eq: 1 Ct(t ) = C ae-ka(days ) + C Se-ks(days ) + C re-kr(days) where, C t(days ) = total soil organic C; C a, Cs, and C r represent the C mass in active, slow, and recalcitrant pools and where, k a, ks, and k r are decomposition rates for each fraction (Paul et al., 2000). Next, I determined the first order derivative of equation 1 to estimate Ca, ka, ks, and k r via non-linear regression using the NLIN procedure in SAS 9.4 where the rate change of CO 2 evolution versus time was determined: Eq: 2 Total C mineralization = C a * kae(-k * days) + C s * kse(-ks * days) + C r * kre(-kr * days) where Ca= active C pool, and ka is the decay constant for the active C pool; C s =slow C pool, and ks = the decay constant for the slow C pool; and C r =passive or resistant C pool and kr= the decay constant for the resistant C pool. Acid hydrolysis was used to determine the resistant pool. The 86 slow pool was calculated by subtracting t he passive and active pool from total soil C (C s=Ct-Ca-Cr). Mean residence times (MRT s) were calculated by taking the inverse of the decay constants for the active and slow pools (1/k). Laboratory estimated MRTs were scaled up to the field level by using a Q10 correction (2^ (lab -field mean temp)/10) that utilizes the difference of laboratory temperature (25 oC) and field temperature at KBS and Wisconsin, 9.9 oC and 6.8 oC, respectively (http://data.sustainability.glbrc.org/protocols/122). Automated weather sta tions at both KBS and ARL were used to measure field temperatures. The preferred method for determining MRT of the resistant pool is through 14C dating; prior analysis at the nearby KBS LTER site revealed that MRTs of the resistant soil C pools are thousan ds of years old (Paul et al., 2001). Particulate organic matter Physical size fractionation was used to determine particulate organic matter (POM), which has been shown to reflect both labile and more processed C pools (Cambardella and Elliot, 1992). I used 4 mm sieved soils so as not to disturb most soil aggregate s (Ontle, 2013) and combined 10 g of air -dried soil with 30 mL of 0.05 sodium hexametaphosp hate in 50 mL centrifuge tubes. Tubes were then placed on a shaker for 8 hours at 120 oscillations min -1. Next, I separated three POM fractions, large (>500 ), me dium (250 -500 ), and small (53 -250 ), to capture both labile and more processed carbon pools. I used deionized water to pass the mixture of soil and sodium hexametaphosp hate through stacked 500 250 , and 53 sieves. The materials that were reta ined on each sieve included fine roots and large sand particles. POM fractions were oven dried at 55 ¡C until constant weight. Dried samples were then ground using a mortar and pestle and analyzed for C and N with a CHNSO Analyzer (Costech ECS 4010, Costech Analytical Technologies, Valencia, CA). 87 Statistics Cumulative C mineralization, MRT, slow and active C pools, and POM were analyzed using Proc Mixed of SAS (version 9.4; SAS Institute, Cary, NC, USA). Site, biofuel cropping system, and depth were treated as fixed effects and block as a random effect. Depth was treated as repeated measures. Significant differences were determined at =0.05 and means were -linear regression function in SAS (Proc NLIN, version 9.4; SAS Institute, Cary, NC, USA) was used to estimate the active pool (Ca) and the decay rates for the active, slow, and passive pools. RESULTS Surface cumulative fluxes Cumulative CO2 fluxes for surface soils (0-10 cm) differed by cropping system (Figures 4.1 and 4.2, F=6.14, p= 0.02 ), but not by site (F= 2.93 , p=0.2 ). At ARL, fluxes ranged from 46.2 (±2.9, standard error of the mean) to 75.0 ±13.8 g C g -1 soil day -1 (Figure 4.1). The restored prairie system had significantly greater fluxes compared to all the other syst ems. Switchgrass, early successional, and native grasses all had significantly greater C fluxes compared to the annual systems. Systems with the lowest fluxes consisted of the poplars, miscanthus and all four of the annual cropping systems. At KBS, cumula tive fluxes ranged from 40 ±3.3 to 65 ±7.2 g C g-1 soil day -1 (Figure 4.2). There were only two main divisions amongst the ten systems: the native grasses and early successional systems had significantly greater cumulative fluxes compared to others. 88 Subsurface cumulative fluxes Subsurface cumulative CO2 fluxes at 10 -25 cm, 25 -50 cm, and 50 -100 cm depths amongst the corn, switchgrass, native grasses, and restored prairie systems yielded different trends at the two sites (Figure 4. 4). At ARL, in th e 10 -25 cm depth all three perennial systems had significantly greater cumulative fluxes compared to the corn system ( p<0.05 ), while there were no significant differences at 10 -25 cm amongst the cropping systems at KBS. At the 25 -50 cm and 50 -100 cm depths , all four systems were statistically similar to one another at both sites. The interaction of site by cropping system by depth was significant (F=3.0, p=0.003), and was likely due to significantly different fluxes under switchgrass and corn between the tw o sites at 10 -25 cm (p<0.05). When averaging by site and cropping system, flux variability was lowest in the top surface depths, where mean coefficients of variation (CVs) were 0.13 ±0.2 and 0.11 ±0.2 for 0 -10 cm and 10 - -50 cm depth where the mean CV was 0.24 ±0.03. CVs were greatest at the deepest depth, where the mean CV was twice as high as at the surface (0.33 ±0.05). Cumulative flux per soil C Cumulative fluxes on a gravimetric basis did not differ between the two sites. Thus, I calculated cumulative fluxes expressed per total soil C and found significantly greater values at KBS (Figure 4.3, F=154.7 , p=0.006 ). Fluxes ranged from 3.5 ±0.7 to 4.9 ±!0.3 mg C g -1 soil C day -1 at KBS compared to 1.9 ±0.2 to 3.3 ±0.5 mg C g -1 soil C day -1 at ARL. There was also an overall significant cropping system effect (F= 4.27, p=0.002 ), with noteworthy trends among cropping systems at both sites. At KBS, the div erse perennial, corn, and miscanthus systems had significantly higher fluxes compared to the poplar, switchgrass, and the rotated corn, soybean, 89 and canola systems. Trends were very similar at ARL , where the restored prairie, switchgrass, native grasses, a nd miscanthus systems had significantly greater fluxes per total C compared to the annual and poplar systems (Figure 4.4). Trends of surface fluxes over time Most systems seemed to stabilize at a low CO2 flux by incubation day 322 but a few systems had fluxes that were still decreasing ( Figures 4.8 -4.27). Furthermore, flux variability was greatest towards the end of the incubation for most systems. In general, the decline in CO 2 fluxes near day 100 diffe rentiates the active and slow C pool. The stabilization of CO 2 flux, which for most crops is represented by an asymptotic line close to but not equal to zero , indicates the presence of the slow C pool. The active C p ool The active C pool significantly differed by system (Figure 4.5, F=6.8, p<0.0001), but there was no site effect (F=2.7, p=0.3). At ARL, there were no distinct trends between the annual and perennial cropping systems , except for the restored prairie syst em, which had the largest active C pool (631 ± 134 g C g -1 soil) compare d to all other cropping systems except the soy bean system. In addition, the poplar and native grasses had a larger active C pool compared to corn (Figure 4.5). At KBS, there was a cle ar difference between the diverse perennials plus the poplar system compared to the monoculture perennials and the annual systems (Figure 4.5 ): the diverse perennials had over twice the amount of active C compared to the other systems. At ARL, the active C pool comprised between 1.9 and 2.7% of the total C pool (Table 4.1). The continuous corn system contained the greatest percentage of C in the active pool. 90 Proportionally, KBS stored more C in the active pool compared to ARL, where percentages of total C ranged from 1.7 % in the continuous corn to 5.7 % in the restored prairie of total C. The slow C p ool Although the size s of the slow C pool at KBS and ARL were statistically indistinguishable (Figure 4.6, p=0.2, F= 2.7), the proportion of C in the slow pool substantially contrasted for the two sites. The slow C pool at ARL accumulated between 27 and 43% of total C compared to KBS, which accumulated between 39 and 55 % (Tables 4.1 and 4.2 ). C a ccumulation rates significantly differed by crop ping system (F=6.9, p<0.0001), however distinct trends were not as visible in the slow pool compared to the active pool. At ARL , the poplar an d early successional systems had significantly greater accumulation in the slow C pool compared to the other syste ms (Figure 4.6). The next group consisted of the restored prairie, switchgrass and corn, which had significantly greater C compared to the native grasses, miscanthus and the other annual systems (Figure 4.6). At KBS, the poplars had significantly gre ater C in the slow pool compared to all the other systems except the native grasses and early successional systems. Although not significant, the diverse perennials, with the exception of the restored prairie, tended to have greater accumulation in the slo w C pool compared to the annuals and monoculture perennials. The marginally significant site by cropping system interaction (F=2.1, p=0.06) was likely because the poplar and early successional systems at ARL had significantly greater C accumulation than the Poplar and early successional systems at KBS (p=0.03 and p=0.005 ). 91 Non -hydrolyzable (resistant) C pool Only site and depth are presented in Figure 4.7 because I did not detect any differences amongst the systems for the resistant pool. The resistant C pool was significantly greater at ARL compared to KBS, accumulating 2.2 times more C in the 0 -10 cm depth. On average, the resistant pool at ARL consists of 69% of the total C compared to 52% at KBS. Significant differences between the two sites were evident at every depth except 50 -100 cm (Figure 4.7). Mean residence time The persistence or mean residence time (MRT) of the active C pool differed by cropping system (Table s 4.1 and 4.2, F=4, p=0.001) but not by site (F=2.7, P=0.2). At ARL, the soybean and poplar systems had the longest active C MRT at 63 ±10.8 and 58 ±6.0 days, respectively. The other systems had MRTs that ranged from 27 -46 days, whereas Miscanthus had the shortest MRT. A t KBS, the poplar system had an MRT of 78 ±18.8 days and was significantly greater than all of the other cropping systems except for the native grasses, which had an MRT of 69 ±13.4 days. Continuous corn had the shortest MRT of 33.4 ±6.7 days. MRTs of the slow C pool did not differ by site (F= 1.3 , p=0.4) or cropping system (F=1.06, p=0.4), although there were some noteworthy trends. For example, at ARL the longest MRTs for the slow C pool were 4.5 and 4.2 years for the poplar and early successional systems. At KBS, pairwise comparisons revealed th at despite no overall cropping system effect, the native grasses had a significantly longer MRT of 7.9 ±4.5 years compared to all other systems (Table 4.2.) 92 Particulate organic matter fractions Across the three POM size fractions, significant differences were more apparent by site than cropping system (Table 4.3 -4.6). For example, at the 0 -10 cm depth ARL had significantly greater POM -C concentrations (g C kg -1) in the large and medium sized fractions (>500 and 125-500 ) compared to KBS (Table 4.3, F=92.1, p<0.0001 and F=11.4, p=0.005). The opposite occurred in the small fraction (53 -125 ), where KBS had significantly greater POM -C concentrations (g C kg -1) compared to ARL (F=564.9, p =0.002). Overall systems differences were only evident in the small fraction (F=17, p <0.0001) whereas the poplar system had significantly greater POM -C than continuous corn at surface depths. Differences were also visible at 25 -50 cm depth at KBS, where c ontinuous corn had POM -C concentrations of 14.9 g C kg-1, compared to 9.4 g C kg -1 of the restored prairie systems and 8.1 of both the switchgrass and native grasses systems (Table 4.5). Overall, at the surface, ARL accumulated more C in the large and medi um fractions where concentrations were up to 19 and 8 g C kg -1 in the large and medium size fractions, with lowest accumulation occurring in the 53 -125 size class (2.6 g C kg-1). At KBS, the opposite occurred, in that the large and medium fractions had POM -C concentrations that were substantially lower than the C in the smaller fraction. For example, the small fraction had concentrations of up to 3.9 g C kg -1 compared to 2.4 g C kg -1 and 1.4 g C kg -1 in the large and medium fractions, respectively. DISCUSSION Overall, diverse perennial systems had substantially greater C accumulation in the active pool compared to both annual systems and monoculture perennial systems, especially at KBS with its less fertile soils. Within monoculture systems, the re were no active C pool differences 93 between annual and perennial crops. Differences in C accumulation between annual and perennial systems were less apparent in the slow pool. However, the p oplar system had significantly greater slow C compared to all ann ual row crop systems at both A RL and KBS. My first hypothesis that perennial systems have greater active and slow C pools was not supported because there were several perennial systems that had statistically similar C pools to the annual systems at both sites. In addition, I did not find any evidence for faster C accumulation at ARL compared to KBS ; instead , I found that C accumulation under perennials was faster relative to annuals at KBS. However, the expectation that C accumulation would be greater in systems with more diversity was supported for the active C pool at both sites, though the trend did not hold for the slow C pool . Active C pool Five years after establishment at KBS, the lower fertility site, the diverse perennial and poplar syst ems had 2.5 times more accumulation in the active C pool at the 0 -10 cm depth relative to the annual systems and monoculture perennials, which were statistically similar to one another. The similarity in active C accrual among annual systems and monocultur e perennials is surprising because several studies have demonstrated greater C accumulation under switchgrass and miscanthus compared to corn (Liebig et al., 2004; Follett et al., 2012). One explanation for the lack of difference here could stem from the n o-till management in the annual systems, which reduces soil C losses (Follett et al., 2012). Bonin and Lal (2012) also found similarities in C accumulation between corn and switchgrass, suggesting that no -till corn systems have the ability to accumulate s imilar amounts of C compared to monoculture perennials in surface soils. That the poplars at KBS behaved more like the diverse perennial systems than the other monoculture perennials is curious, but is probably because of greater diversity than the other m onoculture 94 perennials. Although the poplar system was planted as a monoculture and its overall biomass is dominated by Populus sp. , the understory nevertheless contains six different herbaceous species that provide 24% ground cover. Thus, while poplars are the dominant species, the system resembles a polyculture more than a true monoculture. Greater C accumulation under perennials compared to annuals has been shown in several studies ( Grandy and Robertson, 2007; A nderson -Teixeira et al., 2009; Collins et al., 2010 ). However, the fact that diversity plays a crucial part in these differences was unexpected and to my knowledge has only been shown in grassland and forest systems (Fornara and Tilman, 2008; Steinbeiss et al., 2008, He et al., 2013 ), and not before in intensive cropping systems. One factor that contributes to greater C sequestration under perennials relative to annuals are extensive root systems that have 3 to 8 times greater biomass (Dupont et al., 2014; Culman et al., 2010; Anderson -Teixeira et al., 2013) in addition to year -round ground cover. Root biomass differences among perennial crops have not been intensively studied and thus are less clear. Why might diverse perennial systems accumulate more act ive C than monoculture perennial systems? One explanation is root productivity. Fine root production results from this site reveal that the diverse crops allocate more biomass to roots compared to mon oculture perennials (Sprunger, C hapter 5). Since abovegr ound net primary productivity (ANPP) is equal among cropping systems, except for the miscanthus system, which has larger ANPP (Sanford et al., in press), I can conclude that greater belowground C inputs are the primary driver for enhanced C accumulation un der the diverse perennial systems. Reasons for greater root production under diverse cr opping systems are poorly understood. However, a possible explanation for greater C accumulation in the restored prairie system could be a result of greater fine r oot production due to plant competition 95 4 grasses (Steinbeiss et al. 2008 and Fornara and Tilman, 2008). Given that the restored prairie has C3, C4, an d legume species, the legumes could be facilitating increased nitrogen and stimulating more fine root production leading to greater belowground C. Greater C accumulation in the other diverse species systems where legumes are absent (poplar, native grass, a nd early successional systems) could be consistent with principles put forth by de Kroon et al. (2012), who argue that root foraging activity will be intensified in mixed species systems, where competitive root networks are established due to greater nutri ent demand. At ARL, differences were much less visible between the annual and perennial systems, with only the restored prairie accumulating more active C than the majority of the other systems. One reason for less differentiation at ARL compared to KBS could be that the mollisols found at ARL are extremely high in soil organic matter. For example, 0 -10 cm depth baseline soil C at ARL was 22.4 g C kg -1 compared to 14.3 g C kg -1 at KBS. Thus, Arlington soils could be approaching C saturation, which i mplies that the system does not have the capacity to stabilize additional C inputs as soil C (Stewart et al., 2007). Sandier soils such as those at KBS may be able to build C at a quicker rate after disturbance or changes in management because they are less likely to be close to their maximum C storage capacity (Anderson -Teixeira et al., 2009; Johnston, 2011). Clay soils, on the other hand, will build C at a much slower rate as C approaches equilibrium (West and Six, 2007). Surface soils a t KBS a re 63% sand compared to 25% sand at Arlington. Although the active C pool turns over rapidly, increases in the active C pool will eventually result in greater accumulation of C in more recalcitrant pools if management remains the same. For example, a s the active C p ool increases, a greater proportion of C will transfer into the more 96 recalcitrant pools of C through physical breakdown of organic material and microbial ly mediated processes (Grandy and Neff, 2008). This filtering effect of molecular C compounds is driven by selective microbial degradation, whereby more recalcitrant pools accumulate in the slow and passive pools of C. Slow C pool Differences between the annual and perennial systems in the slow C pool were much less pronounced at KBS; only the poplars had significantly greater slow C pool accumulation compared to other systems. Although not significant, the native grasses and ea rly successional systems had slightly greater slow C pool accumulation compared to the annuals and monoculture perennials, following trends visible in the active C pool. There were no clear trends between annuals and perennials at A RL, but the poplar and early successional systems had substantially greater C compared to all other systems. The restored prairie system had a lower asymptote compared to the other systems at both ARL and KBS (see appendix), which could reflect more C accumulation in the active and slow C pools and less in the passive pool. At both sites, the poplars had twice as much slow C as the other systems. Poplars are the only woody species and previous experiments at the nearby KBS LTER site have also shown that poplars are effective at sequestering C. In the first ten years of establishment, the KBS LTER poplar system added between 32 to 44 g C m -2 y-1 to the total surface soil C pool (Robertson et al., 2000). Twelve years post establishment, Grandy and Robertson (2007) found that poplar s accumulated 37% more total C relative to conventional row crops in the top 5 cm. My findings demonstrate that in the first five years of establishment poplars are accumulating twice as much C in both the active and slow pool in the top 10 cm of soil rela tive to no -till corn. 97 Given that the aboveground biomass is removed post harvest in the poplar systems, this accumulation of C in the slow pool is likely due to coarse and fine root production and turnover. However, fine root production results from this site (Sprunger, Ch apter 5) show that poplars produced fewer fine roots compared to the other polyculture systems. Thus, this belowground C accumulation could be a function of quality rather than quantity. Results from a decomposition experiment in Que bec showed that hybrid poplar roots have a high lignin to N ratio, which could lead to reduced microbial activity and slow the overall rate of decomposition (Camire et al., 1991). Although not always significant, poplars tended to have longer mean residenc e times in both the active and slow C pools compared to other systems, which corroborates findings of Paul et al. (1999). The slow C pool can be altered by management but is generally associated with more stabilized pools of C, which greatly influen ces long -term C sequestration (Wander et al., 2004). The slow C pool is also largely influenced by physical protection (Grandy and Robertson, 2007), which will give systems with more extensive roots an advantage for building C over time, since roots play a n important role in regulating aggregate formation and physi co-chemical protection of soil organic matter (Rasse et al., 2004). Because of this association with more recalcitrant forms of C, it can take several years for gains in the slow C pool to be dete ctable. Thus, it is reasonable to expect detectable increases in the slow C pool over longer periods of time in these perennial cropping systems. Passive C pool I determined the resistant pool by conducting acid hydrolysis analysis on post -incubation soils. Although I did not detect any differences between the two sites for active and slow C, I found substantial differences between ARL and KBS within the resistant C pool. The fact that I 98 did not detect any differences between systems is not surprising , given that C in the resistant pool is typically associated with inorganic materials in soil and is generally not influenced by short-term management or biological activity (Wander, 2004). I did not calculate MRT for the resistant C pool because 14C datin g can provide a more accurate MRT for this C pool than those determined from the decay rate constant s. Paul et al. (1999) found that MRTs for soil C in soils high in clay like those found in ARL were about 2840 years and prior 14C dating at the KBS LTER si te showed an MRT of 1435 years. The high clay content at ARL is likely the reason for high C stabilization in the resistant pool (Collins et al., 1999). The amount of total C found in the resistant pool also differed between the two sites. At KBS, th e resistant pool accounts for 52% of total C. Nearly identical percentages have been reported from work at the KBS LTER. For example, Paul et al. (1999) found that the resistant pool was 56% and 53% of the total C pool for corn and never -tilled systems. In contrast, 69% of C at ARL is stored in the resistant pool. Thus, while C accumulation in the active C pool is occurring quicker under diverse perennials at KBS, ARL is more effective at stabilizing C overall, which is also supported by the amount of C tha t is respired per gram of total C. Little evidence for active C at lower depths Patterns amongst corn, switchgrass, miscanthus, and restored prairie in the top two depth strata (0-10 cm and 10 -25 cm) differed between the two sites. In the 0 -10 c m depth at Arlington, cumulative fluxes increased with diversity, whereby corn had the lowest fluxes and restored prairie had the greatest fluxes. At ARL, the significantly greater C fluxes in the 10 -25 cm stratum for all three perennial systems compared t o corn supports the hypothesis that perennial systems have greater C accumulation compared to annual systems. However, I found no evidence for increasing C fluxes with greater diversity at 10 -25 cm, which refutes my diversity hypothesis. At 99 KBS in the 0 -10 cm depth, cumulative flux patterns did not increase with diversity, with the greatest cumulative flux found in the native grass system, followed by the restored prairie. The corn and switchgrass had the lowest fluxes and were statistically similar to one another. At the 10-25 cm layer I found no differences between the four systems. The fact that I detected differences below the surface layer at ARL suggests that soil C is more evenly distributed between 0 -10 cm and 10 -25 cm compared to KBS and is a reflec tion of the deep A horizon often found in mollisols, which extends to 36 cm at ARL. Below the 10 - 25 cm stratum , I did not detect any differences at either site. There are two plausible explanations for this lack of difference in cumulative C at depth. First, C fluxes were substantially smaller at subsurface depths due to inherently lower C concentrations , and second, fluxes were more variable at depth, suggesting that larger sample sizes are needed in order to detect differences at subsurface horizons ( Kravchenko and Robertson, 2010; Syswerda et al., 2011). Non -linear regression was only reported for surface depths because active C pool (Ca) estimates were highly variable at 10 -25 cm and I did not detect an active C pool in the 25 -50 cm and 50 -100 cm depths. In fact at KBS, the acid hydrolysis and total C concentrations were not significantly different from one another, indicating that C at lower depths is largely comprised of the resistant C pool. Particulate organic matter p atterns In general, POM results did not correspond to patterns of C mineralization from the long -term incubation. On the other hand, POM results were more simil ar to acid hydrolysis findings, with large site differences but no noteworthy differences amongst the systems. One reason for this difference is methodology, because flux data are very sensitive to changes in management (Culman et al., 2013), while physic al size fractionation techniques and acid hydrolysis are 100 associated with organic and inorganic material and as a result reflect more recalcitrant C. Thus, POM appears not to reflect short -term changes in soil C cycling in these systems. Management i mplications My results demonstrate that diverse perennial cropping system s could be used to increase soil C in low fertility soils and marginal landscapes in particular, which has important implications at multiple scales. In terms of energy policy, cellulosic biofuels grown on marginal lands do not compete with food production (Robertson et al., 2008), have a large climate benefit (Gelfand et al., 2013), can produce biomass yields comparable to corn (Bonin and Lal, 2012; Sanford, in press), and also provide additional ecosystem services such as reduce d nitrate leaching (Smith e t al., 2013) and biodiversity benefits such as pollination and biocontrol (Werling et al., 2014). To my knowledge, these findings are the first to report that polyculture second -generation biofuels are more effective at accumulating C than monoculture pere nnials in moderate fertility environments. Relative to corn, polycultures accumulated over twice as much C in the active pool. Furthermore, t hese findings further suggest that restoring prairies in both high and low fertility soils leads to substantial sho rt-term C sequestration. Finally, my results support the notion that C can be accumulated more rapidly in soils lower in fertility. Soil C stocks continue to decline globally and strategies are needed in order to replenish the total C pool. This work demonstrates that diverse systems could be used as a means to sequester C over short and long -term time frames. 101 CONCLUSIONS 1. Soil C gains in the active pool occurred more quickly at the low fertility site (KBS) compared to the high fertility site (AR L). 2. Five years post -establishment, the perennial polyculture systems at KBS, the lower fertility site, had 2.5 times more C accumulation in the active pool compared to no till annual row crops and monoculture perennial systems. 3. Annual row crops and monoculture perennial systems had similar rates of active C accumulation, demonstrating a no -till advantage rather than perenniality per se. 4. At ARL, the site higher in fertility, differences in the active C pool between annual and perennial systems were o nly evident in the restored prairie system, possibly because of C saturation due to soils high C soils. 5. Differences between annual and perennial cropping systems were much less pronounced in the slow C pool. However, at both sites, the poplar system had the highest slow C pool accumulation. 6. ARL , the site higher in fertility, had significantly greater C accumulation in the resistant pool compared to KBS at every depth interval except 50 -100 cm. 7. Pattern s of particulate organic matter concentrations did no t correlate with long -term incubation results, where large site differences were visible in each fraction. This indicates that biological fractionation is more sensitive to management and crop effects than are physical and chemical fractionations. 102 APPENDIX 103 Table 4.1 Mean Residence Times for surface soils (0 -10 cm) of ten biofuel cropping systems at ARL for the active and slow C pool. Active C Slow C System % of Total Lab MRT Field MRT % of Total Lab MRT Field MRT %C days days C years years Corn 2.7 41.7 (3.8) b 147.4 (13.4) 27.1 2.1 (0.5) a 7.4 (1.6) Corn -Soybean -Canola 2.1 38.7 (8.3) b 136.6 (29.3) 19.6 2.1 (0.3) a 7.4 (0.9) Soybean -Corn -Canola 2.2 62.7 (10.8) a 221.2 (38.0) 27 2.6 (0.7) a 9.1 (2.5) Canola -Corn -Soybean 2.6 46.3 (7.9) ab 163.6 (28) 24 3.2 (0.3) a 11.3 (1.2) Switchgrass 2.6 31.4 (1.4) b 111.0 (5.0) 30.2 2.3 (0.5) a 7.88 (1.8) Miscanthus 2.5 27.0 (4.0) b 95.5 (14.2) 16.8 1.4 (0.3) a 4.9 (0.9) Poplar 2.1 57.5 (6.0) a 202.9 (21.1) 43.9 4.5 (0.3) a 15.7 (0.9) Native Grasses 2.8 39.3 (7.6) b 138.6 (26.9) 25 2.1 (0.13) a 7.4 (0.5) Early Successional 1.9 35.0 (5.1) b 123.5 (17.9) 41.9 4.2 (1.2) a 14.6 (4.4) Restored Prairie 2.0 39.3 (4.6) b 138.6 (16.2) 32.4 2.8 (0.3) a 9.9 (0.9) 104 Table 4.2 Mean Residence Times for surface soils (0 -10 cm) of ten biofuel cropping systems at KBS for the active and slow C pool. Asterisks represents (n=1). Active C Slow C System % of Total Lab MRT Field MRT % of Total Lab MRT Field MRT C days days C years years Corn 1.7 33.4 (6.7) c 94.9 (19.1) 41 3.1 (0.5) b 8.8 (1.5) Corn -Soybean -Canola 4.4 27.3* 77.7* 47 3.1* 9.0* Soybean -Corn -Canola 3.3 51.5 (9.4) bc 146.2 (26.6) 44 2.5 (0.1) b 7.2 (0.4) Canola -Corn -Soybean 3.3 33.9 (1.7) bc 96.5 (4.7) 40 2.7 (0.2) b 7.7 (0.6) Switchgrass 3.4 54 (12.8) bc 153.0 (36.2) 41 3.1 (0.3) b 8.9 (0.9) Miscanthus 4.0 36.8 (4.1) bc 104.4 (11.9) 41 2.2 (0.8) b 6.4 (0.8) Poplar 4.9 78.2 (18.8) a 222.2 (53.3) 54.5 3.3* 9.4 Native Grasses 5.4 69.2(13.4) ab 196.4 (38.1) 45.5 7.9 (4.5) a 22.6 (12.8) Early Successional 4.0 40.1 (7.9) bc 113.7 (22.6) 48 2.9 (0.8) b 8.1 (2.4) Restored Prairie 5.7 55.8 (1.9) b 158.4 (5.3) 38.7 3.6 (0.5) b 10.1 (1.4) 105 Table 4.3 Surface soil (0-10 cm) particulate organic matter C concentrations (means and standard errors) for ten biofuel cropping systems at ARL and KBS. ARL KBS System Large (>500 Medium (125-500 Small (53-125 Large (>500 Medium (125-500 Small (53-125 g C kg -1 Corn 11.6 (0.5) b 6.6 (1.7) a 1.5 (0.2) a 1.5 (0.2) a 0.6 (0.08) a 2.4 (0.08) b Corn -Soybean -Canola 11.4 (2.6) b 6.5 (2.0) a 2.6 (1.4) a 2.1(0.2) a 0.9 (0.05) a 2.8 (0.1) ab Soybean -Corn -Canola 15.3 (3.9) ab 5.9 (2.5) a 1.7 (0.6) a 1.3 (0.12) a 0.8 (0.08) a 2.9 (0.2) ab Canola -Corn -Soybean 8.9 (3.8) b 3.9 (0.2) a 1.6 (0.2) a 2.3 (0.2) a 0.9 (0.1) a 3.4 (0.5) ab Switchgrass 11.6 (0.9) b 6.4 (0.4) a 1.4 (0.3) a 2.4 (0.2) a 0.8 (0.1) a 2.9 (0.3)ab Miscanthus 15.7 (0.9) b 8.4 (1.2) a 1.3 (0.3)a 1.4 (0.2) a 0.8 (0.04) a 2.6 (0.2) ab Poplar 13.5 (1.6) a 7.6(1.1) a 1.4 (0.1) a 1.4 (0.2) a 1.4 (0.3) a 3.9 (0.7) a Native Grasses 13.8 (2.3) ab 7.7 (1.9) a 1.2 (0.2) a 1.3 (0.4) a 1.2 (0.3) a 2.7 (0.4) ab Early Successional 15.1 (1.1) ab 7.5 (0.6) a 1.7 (0.2) a 1.9 (0.2) a 1.1 (0.08) a 3.2 (0.3) ab Restored Prairie 19.1 (3.8) a 7.3 (2.0) a 1.5 (0.3) a 0.51(0.1) a 0.8 (0.08) a 2.8 (0.05) ab 106 Table 4.4 Particulate organic matter C concentrations from 10 -25 cm, 25 -50 cm, 50 -100 cm depths (means and standard errors) for four biofuel cropping systems at ARL and KBS. ARL KBS System Large (>500 Medium (125-500 Small (53-125 Large (>500 Medium (125-500 Small (53-125 g C kg -1 10-25 cm Corn 8.3 (1.2) a 2.2 (0.5) a 1.3 (0.4) a 0.4 (0.1) a 0.2 (0.02) a 1.2 (0.06) a Switchgrass 9.5 (0.56) a 2.5 (0.6) a 0.9 (0.3) a 0.6 (0.1) a 0.3 (0.05) a 1.05 (0.2) a Native Grasses 4.4 (0.06) b 1.6 (0.08) a 0.9 (0.3) a 0.5 (0.1) a 0.4 (0.04) a 1.4 (0.1) a Restored Prairie 6.7 (0.84) b 2.5 (0.56) a 0.8 (0.1) a 1.0 (0.6) a 0.2 (0.06) a 0.9 (0.1) a 25-50 cm Corn 2.7 (0.7) ab 1.3 (0.2) ab 0.3 (0.05) a 6.8 (0.3) a 5.3 (0.6) ab 14.9 (0.2) a Switchgrass 4.6 (0.7) a 2.7 (0.7) a 0.2 (0.02) a 6.3 (0.2) a 5.4 (0.52) ab 8.1 (0.1) c Native Grasses 2.4 (1.2) ab 2.0 (0.9) ab 0.2 (0.05) a 8.7 (2.9) a 7.2 (2.9) a 8.1 (1.5) c Restored Prairie 0.5 (0.4) b 0.3 (0.1) b 0.1 (0.008) a 7.0 (3.2) a 3.9 (1.3) b 9.4 (0.9) b 50-100 cm Corn 1.3 (0.2) a 0.6 (0.2) a 0.4 (0.2) a 0.3 (0.1) a 0.2 (0.09) a 0.4 (0.08) a Switchgrass 0.9 (0.6) a 0.3 (0.2) a 0.1 (0.04) a 0.4 (0.06) a 0.6 (0.3) a 0.5 (0.1) a Native Grasses 0.2* 0.6 (0.3) a 0.1 (0.008) a 0.4 (0.1) a 0.3 (0.08) a 0.6 (0.3)a Restored Prairie 0.3 (0.2) a 0.2 (0.04) a 0.1 (0.009) a 0.3 (0.06) a 0.2 (0.03) a 0.3 (0.1) a 107 Figure 4.1 C umulative C mineralization from surface soils (0 -10 cm depths) over the course of 322 day incubations for ARL (n=3). Systems with different lowercase letters are statistically different from one another (p <0.05). 108 Figure 4.2 C umulative C mineralization from surface soils (0 -10 cm depths) over the course of 322 day incubations for KBS (n=3). Systems with different lowercase letters are statistically different from one another (p <0.05). 109 Figure 4.3 Cumulative C mineralization for corn, switchgrass, native grasses, and restored prairie gradient at 0 -10 cm, 10-25 cm, 25 -50 cm, and 50 -100 cm depths . Within each site and depth interval, systems with different lowercase letters are statistically different from one another (p <0.05). 110 Figure 4. 4 Cumulative C mineralization per gram of soil C from surface soils (0 -10 cm depths) over the course of 322 day incubations for ARL and KBS (n=3). For each site, systems with different lowercase letters are statistically different from one another (p <0.05). 111 Figure 4.5 The active C pool for surface soils (0 -10cm). Within each site, systems with different lowercase letters are statistically different from one another (p < 0.05). Bars with no letters were not significantly different from one another. Asterisk represents (n=1). Bars are means ± SE. 112 Figure 4.6 The slow C pool for surface soils (0 -10cm). Within each site, systems with different lowercase letters are statistically different from one another (p <0.05). Bars with no letters were not significantly different from one another. Asterisk represents (n=1). Bars are means ± SE. 113 Figure 4.7 The resistant C pool determined by acid hydrolysis and averaged across cropping system. For each depth interval, asterisks represent statistically significant differences across site (p <0.05). Bars with no asterisk were not significantly different from one another. Bars are m eans ± SE. 114 Figure 4.8 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the continuous corn system at ARL. Shaded bands represent standard error from the mean. Figure 4.9 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the corn -soybean -canola system at ARL. Shaded bands represent standard error from the mean. 115 Figure 4.11 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the canola -corn -soybean system at ARL. Shaded bands represent standard error from the mean. Figure 4.10 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the soybean -corn -canola system at ARL. Shaded bands represent standard error from the mean. 116 Figure 4.12 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the switchgrass system at ARL. Shaded bands represent standard error from the mean. Figure 4.13 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the miscanthus system at ARL. Shaded bands represent standard error from the mean. 117 Figure 4.14 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the poplar system at ARL. Shaded bands represent standard error from the mean. Figure 4.15 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the native grass system at ARL. Shaded bands represent standard error from the mean. 118 Figure 4.16 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the early successional system at ARL. Shaded bands represent standard error from the mean. Figure 4.17 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the restored prairie system at ARL. Shaded bands represent standard error from the mean. 119 Figure 4.18 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the continuous corn system at KBS. Shaded bands represent standard err or from the mean. Figure 4.19 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the corn -soybean -canola system at KBS. Shaded bands represent standard error from the mean. 120 Figure 4.20 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the soybean -corn -canola system at KBS. Shaded bands represent standard error from the mean. Figure 4.21 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the canola -corn -soybean system at KBS. Shaded bands represent standard error from the mean. 121 Figure 4.22 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the switchgrass sys tem at KBS. Shaded bands represent standard error from the mean. Figure 4.23 Predicted mean C mineralization over the 322 day incubation period (dotted curve) for the miscanthus system at KBS. 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Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes. Proceedings of the National Academy of Sciences . 111(4):1652 -1657. West, T. O., and J. Six. 2007. Consider ing the influence of sequestration duration and carbon saturation on estimates of soil carb on capacity. Climatic Change. 80: 2541. 130 CHAPTER 5: PLANT DIVERSITY INFLUENCES FINE ROOT PRODUCTION AND BIOMASS ALLOCATION AMONG PERENNIAL BIOFUEL CROPPING SYSTEMS IN CONTRASTING SOILS OF THE UPPER MIDWEST, USA. ABSTRACT Fine roots play a key role in the global carbon (C) cycle because much of the C accumulating in soil is the result of fine root production and turnover. Here I explore the effect of perennial plant diversity on fine root production, timing of peak fine root production and plant biomass allocation to fine roots over a three -year period a t each of two sites in the upper Midwest, USA. Six perennial cropping systems were e stablished in 2008: switchgrass ( Panicum virgatum) , miscanthus ( Miscanthus giganteus ), hybrid poplar ( Populus nigra P. .), a five -species native grass assemblage an early successional community, and a restored prairie . The site in southwestern Michigan is on a moderately fertile Alfisol soil (Kellogg Biological Station; KBS) and the other site is in south central Wisconsin on a highly fertile Mollisol soil (Arlington; ARL) . From 2011 -2013, two sets of in -growth cores were deplo yed each spring and extracted during - late three years at KBS, I found that the restored prairie and the native grasses had the greatest mid -season fine root production (2.58 ± 1.2 and 2.2 ± 0.4 g m -2 day -1, respecti vely , p<0.05, n=5) . Switchgrass, Early Successional, and Miscanthus followed at 1.4 ± 0.5, 1.09 ± 0.2, and 1.06 ± 0.1 g m -2 day -1, respectively . Poplar had the lowest fine root production , averaging 0.9 ± 0.3 g m -2 day -1 across the three years. Similar tre nds were visible at ARL, where the native grasses and the restored prairie system s had significantly greater mid -season fine root production compared to the other cropping systems (p<0.05). In general, diverse cropping systems allocated more 131 biomass to fin e root production compared to the monoculture systems (p<0.05, n=5). Timing of peak fine root production differed by year and site, likely the result of climate differences. Overall, results suggest that systems with higher species diversity have greater fine root production and allocate a relatively greater amount of biomass to fine roots compared to monoculture systems , which could have important implications for C sequestration. INTRODUCTION Fine roots represent 33% of global net primary producti vity (Jackson et al., 1997) and play a key role in the global carbon (C) cycle because the majority of C accumulating in the soil is the result of fine root production and turnover ( Haynes and Gower et al., 1995). Fine roots turn over at least once per yea r, a frequency that has a direct effect on soil C cycling since a portion of the C from senesced roots is incorporated into soil organic matter (Kumar et al., 2006 ). As roots senesce , C enters the soil organic matter pool, which holds twice the amount of C as the atmosphere ( Swift, 2001 ). Across different ecosystem s fine root turnover can account for 30-80% of organic C inputs into soil (Kalyn and Van Rees, 2006). Furthermore, C derived from roots has been found to persist longer in so il compared to C derived from aboveground material (Rasse et al. 2005; Kong and Six, 2010). Thus, it is important to better understanding fine root production and how it influences C sequestration ( Gill and Jackson, 2000 ) and to determine strategie s that might promote root production and C sequestration in various ecosystems (Glover et al., 2010; Kell et al., 2011). One strategy could be to increase crop diversity. The benefits of biodiversity for aboveground production are well known and have been demonstrated in a variety of natural (Tilman, 1996, Hooper and Vitousek, 1997; Catovsky, 2002) and managed (Smith et al. 2008; Werling et al., 132 2014; Fraser et al., 2015) ecosystems. Several have hypothesized that biodiversity could also have a positive eff ect on belowground production. Hooper and Vitousek (1997 ), for example, suggest that root production should be greater in more diverse cropping systems due to plant complementarity effect s, or differences in phenology and nutrient demand . de Kroon et al. (2012) hypothesized that pathogens constrain root growth in monocultures such that root growth is enhanced in mixed species communities. Empirical evidence, however, is scant and often conflicting. For example, Fornara and Tilman (2008) found tha t high diversity grasslands on sandy soils in northern U.S. stored five times more C than monoculture systems due to greater belowground net primary productivity (BNPP), standing root biomass , and more roots below 60 cm . Bessler et al. (2009), on the other hand, found that belowground biomass and root production remained the same across increased species richness in a similar long -term grassland biodiversity experiment in Europe. Increased fine root production has also been documented in forest systems, whe re mixed forest stands have greater standing fine root biomass and production compared to monoculture stands (Liu et al., 2014). In contrast, others working in forest systems have found no difference in root production with increased plant species diversit y (Domisch et al., 2015; Jacob et al., 2013). In this study I used ingrowth cores to explore patterns of fine root production across six perennial biofuel cropping systems that var y in species diversity. Specifically, I test the hypothesis that fine root production is greater in more diverse cropping systems compared to monocultures. I contrast these responses in two different soil types, a moderately fertile Alfisol in southwest Michigan and a very fertile Mollisol in south central Wisconsin. I further test the consistency of these relationships across three growing sea sons, including a drought year. 133 METHODS Site description Cropping S ystem Experiment (BCSE) co -located at the W.K. Kellogg Biological Station (KBS) Long -Term Ecological Research site in southwest Michigan (42 o o Arlington Agricultural Research (ARL) station in south central Wisconsin (43 o o W). Mean annual precipitation and temperature are 1005 mm and 10.1oC at KBS and 833 mm and 7.4oC at ARL . Soils at the KBS site are moderately fertile , fine-loamy mixed, semiactive, mesic Typic Hapludalfs primarily of the Kalamazoo and Oshtemo series ( Rober tson and Hamilton, 2015): Ap (0 -30 cm), E (30 -41 cm), Bt1 (41 -69 cm), 2 Bt2 (69 -88 cm), and 2E/Bt (88 -152). Surface (0 -10 cm) pH is 6.1 and total soil carbon is 1.25 g kg -1 (Sanford et al., in press) and are 63% sand, 31% silt, 6% clay (http://data.sustain ability.glbrc.org). Soils at the ARL site are highly fertile , silt y loam, mesic Typic Argiudolls in the Plano series (Sanford et al, 2012) with five horizons: Ap (0 -23 cm), A (23 -36 cm), Bt1 (36 -48 cm), Bt2 (48 -79 cm), and Bt3 (79 -109 cm). Surface (0-10 cm) soils at ARL are 9% sand, 66% silt, and 25 % clay (http://data.sustainability.glbrc.org ) and in 2008, pH was 6.6 and total soil carbon was 2.2 g kg -1 (Sanford et al., in review). Prior to 2008, both sites were under annual row crops. Experimental design and systems The BSCE was established in the fall of 2008 replicated at each site as randomized complete block designs with five replicate blocks. Treatments are biofuel cropping systems that include annual row crop s, monoculture perennial grasses , and diverse perennial grasses and forbs . In this study I sampled the perennial cropping systems, which includes three monoculture systems and three diverse systems. Amongst the monocultures are switchgrass (Panicum 134 virgatum), miscanthus (Miscanthus ! giganteus), and hybrid poplars ( Populus nigra ! P. species ( Andropogon gerardii, Elymus canadensis, Panicum virgatum, Schizachrium scoparium, and Sorghastrum nutans) , an early successional community that represents the seed bank and natural colonization since establishment at the beginning of the experiment, and a restored prairie system planted with eighteen di fferent native C3, C4, and legume species (http://data.sustainability.glbrc.org/protocols/144 ). Dominant species in the early successional community during this study at KBS included Conzya canadensis and Setaria faberi; and at ARL, included Lactuca serriola , and Elymus canadensis. Dominant species in the restored prairie during this study at KBS included Elymus canadensis, Sorghastrum nutans, Andropogon gerardii; and at ARL, included Elymus canadensis, Ratibida pinnata, Monarda fistulosa, and Symphyotrichum novae -angliae . The Shannon -Weiner diversity index for each system is presented in Table 5.1. Field preparations in the Spring of 2008 included chisel plowing and secondary tillage (Sanford et al., in press). The BCSE consists of 27 m x 43 m (0.12 ha) plots , separated by 15 -m wide mowed alleys planted in turfgrass . The switchgrass, native g rasses, and restored prairie systems were planted in the summer of 2008 with a brillion -type native plant seeder. Seeding rates for switchgrass were 7.5 kg ha -1. Planting densities for the native grasses ranged from 1.6 to 2.4 kg ha -1 and restored prairie planting densities ranged from 0.4 to 1.2 kg ha -1. Both the miscanthus and the poplar systems were planted by hand in May 2008 at 17,200 rhizomes ha -1 and 2,778 cuttings ha -1, respectively. Miscanthus failed at ARL due to winterkill in 2009 (Sanford et al. , in press) and was replanted in Spring 2010. Nitrogen fertilizer (56 kg N ha -1 y-1 as ammonium nitrate) was applied to switchgrass, miscanthus, and early successional systems each 135 June beginning in 2009 and in 2010 for native grasses. The poplars received a single application of ammonium nitrate fertilizer in 2010 at a rate of 155 kg N ha -1 at KBS and 210 kg N ha -1 at ARL. The restored prairie system was unfertilized. Weeds were controlled with herbicide application in swtichgrass, miscanthus, hybrid popla r, and native grass systems. Harvest for switchgrass, miscanthus, native grass, early successional and restored prairie systems occurred in late October at ARL and early November at KBS. Poplars were harvested in December of 2013 at ARL and January of 2014 at KBS. More extensive details on agronomic practices can be found in Sanford et al. (in press). Fine r oot production In-growth cores were used to estimate fine root production. The in -growth cores were constructed of 2 mm #5 plastic mesh plasti c stapled to form a cylinder 5 cm in diameter x 13 cm long (KBS) or 15 cm long (ARL) and closed at the bottom with plastic caps. Cylinders were filled with soil from cores taken to a depth of 15 cm at ARL and 13 cm at KBS. At KBS, soil cores were taken from individual BSCE plots and sieved to 2 mm in the field. The same procedure was used at ARL, except soil cores were taken from a fallow plot adjacent to the BSCE . At each site, in -growth cores were filled with soil from the site mixed with sand in a 3:1 ratio. Cores were vertically inserted in 5 cm diameter holes to 13 cm depths at KBS and 15 cm depths at ARL at six locations per plot within the BSCE experiment. Installation of cores at both sites typically occurred in mid to late April every yea r (Figure B.1) . The in -growth cores were harvested twice within each growing season , once near the end of July, here after referred to as mid-season , and a second time following harvest near the end of October , hereafter referred to as late season. 136 Fol lowing removal , cores were sieved and washed free of soil. Remaining roots (both live and dead) were then dried at 60 oC for two days and weighed. Fine root production values were calculated as : Total fine root biomass / number of days in the field. To estimate root production between the mid-season and late -season samplings I subtracted mid -season biomass from late -season biomass . Abovegro und net primary production Aboveground Net Primary Production (ANPP) was determined from maximum aboveground biomass as detailed in Sanford et al. (in press ) for the herbaceous perennial crops. In brief, ANPP for switchgrass, native grasses, early successional, and restored prairie systems was determin ed in mid -August when the crops reached physiolog ical maturity. At three pre -determined stations, 2.0 x 0.5 m quadrats were placed in an east -west direction, except for Miscanthus for which a 1.5 x 0.6 -m quadrat was used. Within quadrats, plant biomass was clipped to ground level. Biomass was dried at 60 oC for a minimum of 48 hours. The dry weight was then determined and recorded. For poplars, tree biomass in each plot was determined in December 2011 and 2012 by measuring basal diameter and applying an allometric equation relating diameter to mass. To det ermine the equation, five trees per plot were harvested and weighed after measuring basal diameter. Lastly, basal diameter was regressed against mass for all trees. In winter 2013, the entire p oplar plots at KBS were harvested and biomass was calculated by weight. Poplar ANPP values in 2013 at ARL are not available because the trees were infected with a fungal disease. 137 Fine root BNPP:ANPP i ndex Since I calculate d fine root production on a per day basis , and because there is no daily measurement for ANPP , I established an arbitrary fine root BNPP: ANPP index t o compare belowground fine root production per aboveground net primary production . Fine root BNPP :ANPP Index= Fine root production (g m -2 d-1) /Aboveground producti on (g m-2 y-1) where , fine root BNPP is fine root production estimated as described above. The index is a ratio that can be used to make relative comparisons for rates of belowground allocation across the six systems. Root depth d istribution The standing stock of live and dead root biomass was determined at the end of the growing season in late November for select systems . Root biomass was assessed by taking deep core samples with a hydraulic direct -push sampler to a depth of 1 m (Geoprobe ¨; Salina, KS at KBS and Giddings ¨ probe ; Windsor, CO at ARL ). Cores were taken at three locations in each plot (center and adjacent to plant as well as the interstitial space in cases where plant distribution was clumped or in rows ). Cores were then divided into four different depth strata (0-10, 10-25, 25-50, and 50 -100 cm). Roots were washed free of soil over a 2 mm sieve and dried at 60 oC over a two -day period, then weighed. Statistics Mid-season fine root production and the BNPP :ANPP index were t ransformed to reduce heterogeneity of variance. I utilized a square -root transformation and back -transformed after statistical analyses. Thus, geometric means are reported for mid -season biomass and the 138 BNPP :ANPP index. To back transform the standard error , I calculated a 95% confidence interval of the transformed data and then back transformed the interval (Bland and Altman, 1996). Data were analyzed using Proc Mixed of SAS (version 9.4; SAS Institute, Cary, NC, USA). Cropping system and depth were t reated as fixed effects and block as a random effect. For mid-season fine production, fine root:ANPP index, and the difference between late and mid season production, year was treated as a repeated measure. Significant differences were determined at p=0.05 comparison. RESULTS Precipitation At ARL cumulative precipitation during the time that the in -growth cores were installed (April -Oct/Nov) was 451, 491, and 546 mm for 2011, 2012, and 2013, respectively (Figure 5.1A). ARL always received more rain in the first part of the growing season compared to the later portion . At KBS, i n both 2011 and 2013, precipitation was above average between April and late October. In 2012, KBS had a drought early in the growing season , with onl y 152 mm by mid season (Figure 5.7 ). Fine root p roduction Mid-season fine root production significantly differed across the six cropping syste ms at both ARL and KBS (Figure s 5.2 and 5. 3; ARL , F=3.7, p=0.01; KBS, F=4.8, P=0.003). Fine r oot production also significantly varied from year to year (ARL, F=5.3, p=0.009; KBS, F=12.8, p<0.0001), although trends amongst the different cropping systems were similar each year. At ARL, the nat ive grass and restored prairie systems typically had the greatest amount of fine root production (Figure 5.2). In 2011, fine root production ranged from 0.52 to 1.40 g m -2 139 day -1, and the native grass and restored prairie systems had significantly greater f ine root production compared to the miscanthus and poplar systems ( Figure 5.2 , p<0.05). In 2012, the native grass system had significantly greater (p<0.05) fine root production compared to all three monoculture systems with a mean of 1.65 g m -2 day -1 compared to switchgrass (1.26 g m -2 day -1), miscanthus (1.18 g m -2 day -1), and poplar (1.18 g m -2 day -1 Figure 5.2). Similarly in 2013, the native grass and restored prairie systems had significantly greater fine root production compared to miscanthus and poplar systems. In 2012 and 2013, production across the monoculture systems was approximately even. Averaging across year, the native grass system produced the greatest amount of fine roots (2. 3 ± 0.2 g m -2 day -1), while the miscan thus system produced the lowest at 1.2 ± 0.13 g m -2 day -1. There was a strong year effect at ARL (F=5. 3, p=0.0009), which was likely caused by the variability in fine root production amongst the monoculture perennials, as the diverse fine root production was relatively cons istent across the three years (Figure 5.2 ). For example, the poplar system fine root production significantly increased in 2012 and 2013 (p<0.05), while fine root production of switchgrass and miscanthus tended to decrease over time. The diverse cropping systems stayed remarkably stable over the three years, except for the 2013 early successional system, which was lower in 2012 by 40%. At KBS, the native grass and restored prairie systems also produced the greatest amount s of fine roots , except in th e case of r esto red p rairie in 2011 (Figure 5.3 ). In 2011, the native grass system produced significantly more fine roots than all other systems except for the early successional system. In 2012 and 2013, the restored prairie system had greater fine root production than all other systems , except native grasses and switchgrass in 2013. In all years, t he poplar and miscanthus systems had the lowest fine root production , except for poplars in 2013 . 140 Averaging across year s, the restored prairie system produced the greates t amount of fine roots with (2.6 ± 1.2 g m -2 day -1) followed by native grasses (2.2 ± 0.4 g m -2 day -1), while the poplar system produced the lowest at (0.9 ± 0.3 g m -2 day -1). In general, fine root production at KBS was greatest in 2013, w hen production ranged from 1.0 to 4.8 g m -2 day -1, followed by 2011 (Figure 5.3). L owest production occurred in the drought year 2012 when values ranged from 0.8 to 1.3 g m -2 day -1. Differences over time were especially evident for the switchgrass and restored prairie systems, which had significantly greater root production in 2013 compared to prior years (p<0.05). Fine root BNPP:ANPP i ndex The fine root BNPP:ANPP index significantly differed by cro pping system at both sites (Figures 5.4 and 5.5; ARL, F=30, p<0.0001; KBS, F=16.1, p<0.0001). A significant year effect was only evident at ARL (F=72, p<0.001). However, there was a significant crop by year interaction at both sites (ARL, F=5. 1, p<0.0001; KBS, F=2.1, p=0.05 ). At ARL the fine root BNPP:ANPP index in the restored prairie system was significantly greater than in all three monoculture systems (Fi gure 5.4 , p<0.05). In 2011, BNPP:ANPP indices ranged from 5.4 to 27.3, where the restored prai rie had the greatest index and miscanthus had the smallest index. In 2012 and 2013, the diverse perennials alway s had significantly greater indices compared to the monocultures with the exception of the native grasses (index= 44.5) in 2012, which were not signific antly different from the poplars (index=30.9) . Fine root BNPP :ANPP indexes greatly varied from year to year at ARL. Averaging across cropping system, indexes in 2012 were 59% greater than in 2011 and 71 % greater than in 2013. 141 With the exceptio n of switchgrass and miscanthus systems, all system indice s were significantly greater in 2012, compared to the other two years (p<0.05). At KBS, the diverse perennial systems (native grasses, early successional, and restored prairie) always had sig nificantly greater fine root BNPP:ANPP indexes than miscanthus and poplar s ystem s, except in 2013, when the early successional sys tem had a lower index (Figure 5.5, p<0.05 ). The s witchgrass system had a significantly greater index compared to the othe r monocultures, except in 2012. In 2011 and 2012, the native grass system was the only diverse system that had a signifi cantly greater index than switch grass. Amongst the diverse perennial systems , there were no significant differences, except in 2013, when th e early successional system had a substantially lower index compared to the native grass and restored prairie systems . Averaging across year s, the restored prairi e system had the greatest index of 23.6 ± 4.3, while the miscan thus system had the lowest inde x of 4.6 ± 0.8. There was no overall year effect at KBS (F=0.3, p=1.3) , as Fine root BNPP :ANPP indices remained relatively stable over the three years for all systems . However there was a significant interaction (F=2.1, p=0.05), likely caused by certain crops that had indexes that fluctuated through time. For example, pairwise comparisons revealed that switchgrass in 2013 h ad significantly greater indices than in 2011 and 2012 (p=0.02 and 0.03, respectively). Late season vs. m id-season fine root production I calculated the difference between late season fine root production and mid -season fine root production to reveal the seasonal pattern of fine root production in a given growing season. Peak fine root production did not differ among cro pping systems at either site (Table 5.2 , ARL, F=2.0, p=0.1; KBS, F= 1.1, p=0.3 ). However, there were note worthy differences through time, as the year effect was marginally significant at both sites (ARL, F=2.7; KBS, F=2.8, p=0.07 142 p=0.08). At ARL, all cropp ing systems exhibited peak biomass in by the middle of the growing season in both 2011 and 2012. However, in 2013 the switchgrass, miscanthus, native grasses, and restored prairie systems had greater root production in the later part of the growing season. In contrast, peak production at KBS tended to occur at the later part of the growing season for almost every crop, especially in 2012. Exceptions where peak fine root production occurred in the middle of the growing season included the poplar and native g rass systems in 2011, and the restored prairie system in 2011 and 2013. Root depth distribution Root biomass was strongly concentr ated at surface depths for the switchgrass and miscanthus systems (Figure 5.6 A). For example, 77% and 78% of total mi scanthus root biomass were found in the top 10 cm at ARL and KBS, respectively. The switchgrass system root distribution at ARL was very similar to that of the miscanthus system where 77% of total root biomass was found in the top 0 -10 cm depth. Switchgras s distributions at KBS were more even in the top two depth intervals, where 67% of total root biomass was found in the top 0 -10 cm depth interval, 79% was found in the top 0 -25 cm depth interval, and 89% was found in the top 0-50 cm depth interval. Total root biomass between 50 and 100 cm depths ranged from 4 -9% for both systems at both sites, except in the switchgrass system at KBS, where 11% of total root biomass was found below 50 cm. Root biomass distribution in an identical poplar system near the BSCE at the KBS LTER site (Robertson and Hamilton, 2015) was more evenly distributed throughout the soil profile , with a greater relative percentage of deeper roots compared to the misca nthus and switchgrass systems (Figure 5.6B) . For example, 57% of total root biomass was found in the top 10 cm, 68% in the top 0 -25 cm, and 85% in the top 0 -50 cm. 143 DISCUSSION The native grasses and the restored prairie systems consistently produced greater amounts of fine roots c ompared to the monoculture systems (especially poplar and miscanthus) at both ARL and KBS, while in the early successional systems fine root production wa s generally more similar to the monoculture systems . At ARL, the native grass , early successional community, and restored prairie systems all allocated greater fine root production per aboveground net primary productivity (BNPP:ANPP) compared to the monocu lture perennials in 2012 and 2013. At KBS, the restored prairie and native grass sy stems had greater BNPP:ANPP indices compared to the monoculture perennials in 2011 and 2012. In contrast, the early successional community typically had greater BNPP:ANPP th an the miscanthus and poplar systems but had statistically similar indexes to switchgrass. Measuring total root biomass to one meter revealed that 56% of roots were in the top 0 -10 cm for the poplar system and almost 80 % for the switchgrass and miscanthus systems. Thus, the 15 cm deep in -growth cores used in this study sufficiently captured the majority of fine root production in the switchgrass and miscanthus systems and over half in the poplar systems. Diversity influences mid -season fine root productio n and allocation In general, the native grasses and restored prairie systems produc ed more fine roots than miscanthus, switchgrass, and poplar systems at both sites over all years. Fine root production in the early successional system was more simi lar to that in monoculture systems . Thus, while not all of the diverse cropping syste ms differed from the monocultures , the mixed grass systems consistently produ ced more fine roots. Although I could find no other studies that compared fine root production between monoculture and diverse perennial cropping systems , a few studies have 144 reported enhanced root production under more diverse forest and grassland ecosystems (Steinbeiss et al., 2008 ; Fornara and Tilman, 2008; Brassard et al., 2013 ; Gamfeldt et al., 2013). Enhanced root production within the native grass and restored prairie system s was likely driven by one or two dominant species rather than species richness . For example, Elymus canadensis was domina nt in both the native grasses and restored prairie systems except in 2013 at ARL. Elymus canadensis and Luctuca serriola were dominant i n the early successional system but species abundance was more evenly distributed compared to the mixed grass systems and thus a dominant species was not as easily identifiable. Similar species distributions were evident at KBS where the early successional system was dominated by species like Conyza canadensis rather than the Andropogon gerardii or Elymus canadensis dominants found in the native grass and restored prairie systems. One explanation for lower fine root produc tion in the early successional system is the greater presence of annual species like Conyza canadensis compared to perennials such as Elymus canadensis , which tend to produce a greater amount of roots (Sainju et al., 1998). At ARL, annuals comprised 6% of total plant composition in the native grasses, 33% in the early successional community, and less than 1% in the restored prairie system. At KB S, annuals accounted for 1% of the native grass system , 79% of the early successional community, and 3% of the restored prairie system. Thus, my findings suggest that diverse systems have greater fine root production, except where annuals are dominant. Even though I did not compare the two sites statistically due to slight differences with the in-growth cores and pseudoreplication concerns, I found similar trends at both locations in terms of root production across the six different cropping systems. This suggests that these diverse 145 perennial systems produce large r amounts of fine roots than monoculture systems , regardless of soil type and climate. These results are consistent with Fornara and Tilman (2008) who found greater fine root product ion with increased diversity in a long -term biodiversity grassland experiment at the Cedar Creek LTER in northern Minnesota, USA. Furthermore, these results support the diversity -productivity hypothesis and the plant complementarity effect hypothesis , both of which posit that systems with more diversity will have greater root production due to differences in rooting depths caused by a variation in phenology and plant resource demand (Tilman et al., 1996 and Hooper and Vitousek, 1997). For example, the syst em greatest in diversity in this experiment was the restored prairie , which consists of C3 forbs and grasses , C4 grasses , and legumes. The different plant functional groups could dictate when different species reach peak biomass, which could ultimately lea d to greater plant nutrient demand. This in turn enhances belowground competition resulting in greater fine root production (Fornara and Tilman, 2008). Belowground allocation The fine root BNPP :ANPP index is an indication of investment in below ground versus aboveground production. Although there were a few exceptions, I generally found that the fine root BNPP :ANPP index was greater in diverse cropping systems compared to the monoculture systems , which suggests that at both sites, plants in diver se perennial systems allocated a relatively greater amount of biomass to roots compar ed to plants in the monoculture perennial systems. This trend contrasts with Bessler et al. (2009), who found a decrease in root:shoot ratios with increased diversity at a n experiment in Germany. Bessler et al. (2009) suggest that the plant complementarily effect led to more available N in the diverse cropping systems , causing a reduction in belowground biomass and greater allocation to aboveground. 146 Implications for carbon sequestration The greater fine root production and relative biomass allocation t o fine root production within the diverse systems could have important i mplications for C sequestration as fine roots are a primary contributor to soil C inputs ( Haynes a nd Gower, 1995). For example, despite the fact that fine roots often make up less than 5% of total biomass, fine roots account for nearly 50% of cycled C in certain ecosystems (Meier and Leuschner, 2008). Fine roots contribute to C accumulation and stabilization through chemical and biophysical processe s. While both above and belowground plant litter rep resents new source s of C, fine roots are composed of complex structures that are more recalcitrant to microbial decomposition compared to aboveground litter (Rasse, 2005). As fine roots decompose, microbial communities selectively degrade the labile forms of C leaving more complex materials behind that subsequently transition into more recalcitrant pools of C (Grandy and Neff, 2008). As a result, a primary contributor to the more recalcitrant C pools are microbial biomas s and by -products such as polysaccharides and lipids that interact with silt and clay fractions and ultimately stabilize soil C (Paul et al., 2015). Thus, the divers e systems that hav e greater fine root production will likely have greater C accumulation and C stabilization over time. Furthermore, in Sprunger (Chapter 4), I found that the diverse perennial systems had 2.5 times more active C accumulation compared to the switchgrass and miscanthus systems, indicating that greater fine root producti on in theses systems contribute to C accumulatio n. Enhanced C sequestration with diversity and perenniality has also been demonstrated in other environmental settings (Fornara and Tilman, 20 08, Collins, 2010, Kong and Six, 2010). For example, Steinbesis et al. (2008) reported that soil C storage increased with species richness in large part due to enhanced root biomass with greater diversity in native grass systems . 147 Timing of peak fine root production I quantified the difference between late and mid seaso n fine root production to determine peak production. A positive value indicates greater root production later in the growing season, whereas a negative number indicates greater root pr oduction in the first part of the growing season. I found that the majority of systems over the three years had peak production in the middle of the growing season at ARL. This suggests that roots were decomposing and turning over in the later stage s of the growing season . At KBS the opposite trend occurred, with the majority of crops producing the g reatest amount of fine roots in the later half of the growing season. A particularly noteworthy trend at KBS was that the greatest amount of late season production occurred during 2012. This was possibly caused by the drought that KBS experienced in the early part of the 2012 growing season , which likely slowed root production . However, when increased rainfall occurred in the second part of the growing season, fine root production was stimulated , which has been shown in several studies (Steinemann e t al., 2015; Fiala et al., 2009; Pavon and Briones, 2000). In addition, contrasting trends occurred at the two sites, suggesting that peak fine root productio n occurrence is also affected by soil type. For example, ARL has greater N availability than KBS and there is some evidence that fine root turnover is faster in systems with greater N accumulation (Brassard et al., 2009). It has been a long -standing view in the literature that peak belowground production occurs in the middle of the growing season, with enhanced root decomposition and turnover in the later stages of the growin g season (Domisch et al., 2015). For this reason, it has been an accepted practice for investigators to sample root production once within the growing season (Solly et al., 2013, Wang et al., 2013; Ravenek et al., 2014) . Others have promoted using sequential coring or 148 the maximum -minimum method for quantifying fine root biomass (Na del hoffer and Raich, 1992, Brassard et al., 2011) to capture changes in fine root production through out the growing season. My results suggest that the former approach could lead to unrealistic measures of fine root production. For example, at KBS , had I only sampled during the middle of the season, I would have underestimated fine root production. My resu lts demonstrate that it is impe rative to sample fine root production at least twice to capture peak production . Root depth distribution One po tential limitation of this study is that the in-growth cores were only 15 cm deep. Thus, I was not able to capture dynamics of root foraging at greater depths . However, Bessler et al. (2009) found that species richness and diversity do not affe ct root bio mass at lower depths. Furthermore, similar research at the KBS site measured fin e root biomass to a depth of 1 m in an annual and perennial system over a three year period, and did not detect an y differences in fine roots in any of the three years, includi ng the drought year (Sprunger, Chapter 2 ). Finally , deep cores from both th e KBS and ARL sites demonstrate approximately 80% of root biomass is found in the top 10 cm for switchgrass and miscanthus and 57 % for the nearby Poplar system. Thus, it seems reasonable to conclude that the in-growth cores captured the majorit y of the fine root production in these systems, and that in -growth cores are certainly sufficient to m ake valid cross -system comparisons. CONCLUSION S Overall, I found that the native grasses and restored prairie systems had greater mid -season fine root production at both fertile (ARL ) and moderately fertile (KBS ) sites, which suggests that more di verse systems produce more fine roots regardless of soil type. Fine root 149 production in the early successional system was more similar to the monoculture systems compared to other diverse perennial systems , which suggests that dominance by annuals reduces system -level fine root production . Over the course of three years, it was clear that the diverse perennials allocated more biomass to fine root production compared to the perennial monoculture stands. These findings are consistent with other studie s that demonstrate that biodiversity plays a key role in fine root production, which in turn has important implications for soil C accumulation. Precipitation distribution across the growing season and soil type seemed to influence peak fine root production , while cropping system did not. At ARL, peak production was typically greatest in the middle of the growing season, when the majority of the precipitation events occurred. In contrast, rainfall patterns were more variable over the three years at KBS, where fine r oot production was typically greatest in the later part of the growing season. Fine root production should be quantified more than once during the growing season to obtain more accurate estimates of fine root peak production . Finally, results underscore th e importance of plant diversity for promoting soil C sequestration in biofuel and other ma naged perennial communities Table 5.2 Difference between end of season fine root production and mid -season fine root production at ARL and KBS. Numbers represent the mean and standard error (in parentheses) for each system. A positive number indicates greater root production at in the later part of the growing season and a negative number indicates greater root production during the middle of the growing season. 150 APPENDIX 151 Table 5.1 Shannon -Weiner diversity index for native grass, early successional, and restored prairie s ystems at KBS and ARL for years, 2011, 2012, and 2013. Year Location System 2011 2012 2013 Native Grasses 1.09 1.34 1.23 ARL Early Successional 1.96 1.97 1.94 Restored Prairie 1.75 1.92 2.23 Native Grasses 1.56 1.79 1.42 KBS Early Successional 1.48 2.40 2.10 Restored Prairie 2.04 2.24 2.20 152 Table 5.2 Difference between end of season fine root production and mid -season fine root production at ARL and KBS. Numbers represent the mean and standard error (in parentheses) for each system. A positive number indicates greater root production at in the later p art of the growing season and a negative number indicates greater root production during the middle of the growing season. Year Location System 2011 2012 2013 Switchgrass -0.3 (1.3) -0.4 (0.7) 1.1 (0.9) Miscanthus -0.4 (0.8) -0.5 (1.1) 1.4 (0.9) ARL Poplar -0.3 (0.2) -2.0 (0.7) -3.1 (1.7) Native Grasses -2.1 (0.9) -1.5 (1.5) 0.4 (1.1) Early Successional -1.8 (0.6) -1.3 (0.6) -0.1 (0.5) Restored Prairie -0.5 (0.7) -1.0 (0.8) -0.4 (1.6) Switchgrass 0.5 (0.8) 1.4 (0.5) 0.2 (1.1) Miscanthus 0.6 (0.6) 0.8 (0.4) 2.3 (0.7) KBS Poplar -0.4 (0.5) 1.1 (0.5) 0.3 (0.7) Native Grasses -0.02 (0.3) 2.2 (0.6) 3.0 (1.6) Early Successional 1.3 (0.5) 0.3 (0.2) 0.2 (0.6) Restored Prairie -0.3 (0.4) 2.1 (0.4) -2.4 (2.1) 153 Figure 5.1 Precipitation during two different intervals of the growing season for which in -growth cores were installed . Beginning to Mid season started when the cores were installed in mid April and ended when the first set of cores were re moved during the middle of the growing season. The Mid to Late season interval covers the length of time between the mid season core removal and the date when the second set of cores were removed at the end of the growing season. 154 Figure 5.2 Mid-season fine root production (geometric mean) for six perennial cropping systems ranging in diversity (switchgrass, miscanthus, poplar, native grasses, early successional, and restored prairie) at A RL in 2011, 2012, and 2013. Error bars represent back t ransformed 95% confidence intervals. Different letters within a given year denote a significant at =0.05. 155 Figure 5.3 Mid-season fine root production (geometric mean) of six perennial cropping systems ranging in diversity (switchgrass, miscanthus, poplar, native grasses, early successional, and restored prairie) at the KBS in 2011, 2012, and 2013. Error bars represent bac k transformed 95% confidence intervals. Different letters within a given year denote a significant at =0.05. 156 Figure 5.4 Fine root BNPP: ANPP Index (geometric mean) of six perennial cropping systems ranging in diversity (switchgrass, miscanthus, poplar, native grasses, early successional, and restored prairie) at ARL in 2011, 2012, and 2013. Fine root BNPP :ANPP ind ices are the ratio of fine root production to ANPP. Error bars represent back transformed 95% confidence intervals. Different letters within a given year denote a significant at =0.05. 157 Figure 5.5 Fine root BNPP: ANPP Index (geometric mean) of six perennial cropping systems ranging in diversity (switchgrass, miscanthus, poplar, native grasses, early successional, and restored prairie) at KBS in 2011, 2012, and 2013. Error bars represent back transformed 95% confidence interva ls. Different letters within a given year denote a significant at =0.05. 158 Figure 5.6 A) Miscanthus and Switchgrass root biomass distribution averaged across three years (2011, 2012, and 2013) to one meter at KBS and Arlington. B) Popla r root biomass distribution to 1.22 meter from the nearby Long -Term Ecological Research experiment at KBS. 159 Figure 5.7 Timing of in -growth core installation and p recipitation at ARL and KBS during 2011, 2012, and 2013. Both sets of cores were installed in late March or early Apr il. The first set of cores (Mid -season) were typically removed in mid July or early August and the second set of cores ( late season) were removed in late October or early November. Arrows indicate when cores were installed and removed. a)=ARL 2011, b)ARL 2012, c) ARL 2013, d) KBS 2011, e) KBS 2012, f) KBS 2013. 160 REFERENCES 161 REFERENCES Asseng, S., J. T. Ritchie, A. J. M. Smucker, and M. J. Robertson. 1998. 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Biodiversity and ecosystem stability in a decade -long grassland experiment. Nature . 441:629632. Wang, H., Z. -X. Chen, X. -Y. Zhang, S. -X. Zhu, Y. Ge, S. -X. Chang et al. 2013. Plant species richness increased belowground plant biomass and substrate nitrogen removal in a constructed wetland. Clean -Soil, Air, Water . 41:657664. 165 CHAPTER 6 : DO TOTAL SOIL CARBON TESTS MEET FARMER MANAGEMENT NEEDS? MEASURES OF ACTIVE CARBON VERSUS STATIC SOIL ORGANIC MATTER POOLS ABSTRACT Farmers are dependent on soil testing for management decisions that influence farm profitability and soil health. Providing farmers with tests that are more management sensitive could improve economic returns and on -farm envir onmental performance. Here I compare two soil carbon (C) measures on 52 Michigan farmer fields: total soil organic matter (SOM) and active C. Total SOM is widely accessible to farmers via university and commercial laboratories, while C mineralization (acti ve C) is not yet commercially available. I used quantitative field -based research and qualitative approaches to determine the effectiveness of total SOM versus active C for deciphering differences in soil C across fields identified by farmers as Best perfo rming, Worst performing, a field of their choice, as well as a non -row crop area. Farmer descriptions of fields were typically based on yield, SOM levels, compaction, and water holding capacity. After soil sampling, laboratory analyses and field observatio ns, I held individual meetings with each farmer to determine field history, management practices, and soil testing history and to explain soil test results. Active C tests detected significant differences between the Best vs. Worst fields (t -test= 5.8; p<0 .0001), while total SOM tests were statistically similar for the Best and Worst fields (t -test=2.8, p =0.07 ). The average coefficient of variation for between the Best and Worst fields for Active C was 0.30 ± 0.03 and was substantially greater than the CV for SOM ( 0.05 ± 0.01), suggesting that active C is a more sensitive test than total SOM. Additionally, the level of agreement found among my field observations, farmer perceptions of 166 soil health and active C test results were strong. The active C tests are more management sensitive and better support farmer perceptions of SOM than do results from the total SOM tests. University and commercial laboratories should consider offering active C tests to provide farmers test results that bet ter inform short -term SOM management decisions. INTRODUCTION Soil health is interchangeable with soil quality, and represents the capacity of soil to sustain plant and animal productivity, maintain or enhance environmental quality, and promote plant, animal, and human health (Doran, 2002; Magdoff and Van Es, 2009). The most effective way to improve soil health is to increase soil organic matter (SOM) because SOM can help to improve physical, chemical, water retention, and biological soil parameters (Rob ertson and Grandy, 2006). For example, SOM leads to the formation of more soil aggregates (Tisdall and Oates, 1982), which increases nutrient retention, improves soil physical structure, and alleviates compaction (Jarecki and Lal, 2010). SOM also enhances microbial activity, which is important for nutrient cycling and crop productivity (Rees et al., 2005). For this reason, farmers rely on SOM indicators as an overall measure of soil health and for predicting agronomic performance (Magdoff and Van Es, 2009). However, SOM is often insensitive to changes in management because it is a large and dynamic pool that consists of carbon ( C) that varies in persistence and decomposition (Wander, 2004). For instance, the smaller and more labile SOM pool consists of recently deposited material that typically decomposes within a year, fluctuates with crop growth, is sensitive to changes in management, and is a predictor of long -term C sequestration (Franzluebbers, 2000). However, the larger and 167 older pools of C persis t in the soil for thousands of years and are insensitive to new management practices . The most widely available SOM indicat or for farmers is total SOM , which is largely comprised of the older and more recalcitrant pools of C. As a result detecting ch anges in SOM after the introduction of new management practices can take many years. Thus, the length of time needed to detect changes in total SOM, has motivated researchers to develop methods that isolate the younger and more active portions of the SOM p ool (Culman, 2013). Since the active C pool represents a large nutrient reservoir that influences other chemical, physical, and biological soil properties , measuring only this pool could be informative for farmers as they consider adopting a variety of dif ferent management practices to improve soil health and boost yields . An increasingly common indicator that is used to determine the active portion of SOM is C mineralization, which can provide an estimate of available C by measuring microbial respira tion (Paul et al., 2000). Culman et al. (2013), for example, detected different C mineralization rates amongst monoculture and rotated cropping systems and differences between conventional and organic management. C mineralization was the best of six labile C and nitrogen (N) measures for predicting agronomic performance. However, it is not yet clear if C mineralization (active C) can detect on -farm differences in soil C that will inform farmers about soil health. Farmer Participation Since the early 1980s there has been a large effort to conduct on -farm research because solving agronomic and soil quality issues on -farm can lead to more realistic results compared to experiments at research stations (Thompson and Thompson, 1999; Wa nder and Drinkwater, 168 2000). Furthermore, there has been a strong effort to introduce different soil quality indicators that are sensitive to changes in management to extension educators and farmers (Doran and Parkin, 1996). However, farmer knowledge is a k ey component for making on -farm research successful and applicable when evaluating soil quality with new soil tests (Gruver and Weil, 2007; Leibig, 1996). For example, in an attempt to match soil quality indicators with farmer perceptions in the Mid -Atlant ic region of the U.S., Gruver and Weil (2007) found strong agreement between farmer perceptions of soil quality and soil quality indicators when comparing fields that farmers had identified as having good versus poor soils. The microbial biomass, anthrone reactive C, and macro aggregate tests had higher levels of agreement with farmer ratings than did soil pH and micronutrient content. Little is known about the effectiveness of active C for informing farmers about soil health and whether it corresponds wit h farmer perceptions and investigator field observations. In this study, I use techniques from action research and soil science to explo re farmer perceptions of soil C , to investigate the underlying reasons behind adopting certain management pr actices to i mprove soil health, and to determine if farmers have access to tests that meet management needs. I combine soil testing with investigator field observations and meetings with farmers to ask : Does the active C test better reflect farmer perceptions of yield expectations and soil health on each field than does total SOM? METHODS Participant selection This study was grounded in a participatory action research framework where Michigan farmers, MSU Extension staff, and MSU researchers worked together to determine if different 169 soil tests corroborated with farmer perceptions of soil health. Thirteen farms across three counties in Michigan, USA participated in this study (Table 6.1). The three counties included Isabella (43 o o o o o 85o types. Farmers were chosen by Michigan State University (MSU) extension agents based on willingness to participate in i nterviews and workshops in exchange for free soil testing. While it is likely true that the farmers who pa rticipated in this study were more invested in soil testing than the av erage farmer, the overall goal of this research is not to generalize farmer res ponses to a larger farmer population. Instead, I intend that results from this study can inform researchers and extension staff on how farmer perceptions of soil quality are reflected in different soil tests and whether this information can be used to stre ngthen soil testing and farmer management decisions. On-farm soil sampling and field observations Initial farm visits consisted of meeting with farmers, field observations, s oil sampling, and when possible, concise interviews with farmers regar ding farm history and soil and crop information. At each farm, I asked farmers to select four fields to include for sampling: a best -performing field (Best), a worst -performing field (Worst), a field of their choice (Choice), and a non-row crop or unmanage d area (NRC). Soils were sampled at the end of May and early June, 2014, spanning three weeks. Five samples were randomly taken from each field. The first sample was taken 4.5 meters from a field edge, while subsequent samples were taken every 4.5 meters by walking diagonally across each field. Samples were taken by digging 15 cm deep pits between cultivated rows and extracting 4 cm wide slices of soil. Next, a trowel was used to cut a rectangular block of soil (15 cm by 10 cm by 4 cm). The five soil sample s were composited by 170 field and mixed thoroughly and then put through a 2 mm sieve. In addition, 30 g were kept for the C mineralization test while 40 g were sent to the MSU soil analysis lab for the total SOM test. At each of the five sampling points, two penetrometer readings were also taken at 15 cm and 46 cm depths to determine surface and subsurface compaction. Field observations consisted of taking photographs of the crop and soil and making descriptive field notes on the condition of crops and soils. Laboratory analyses : total soil organic matter ( SOM ) and active C The Soil and Plant Nutrient Laboratory at MSU utilizes loss on ignition to determine percent SOM. Samples (40 g) were oven dried for 48 hours at 105¼ C, weighed, then heated in a muffle furnace at 550¼ C for five hours. SOM was determined by difference in weight: where SOM L.O.I =(DW I-DWo)/DW I x 100; DWI = Oven dry soil weight (dried at 105 oC) and DWo = Soil weight after ignition at 550 oC. Active C was determined via short -term C mineralization incubations. Ten grams of soil were placed in a 237 mL Mason jar, re -wetted and then incubated for 24 hours at 25 oC. Two analytical replicates were analyzed p er field. Soils were adjusted to 50% water -filled pore -space utilizing the methods described in Franzluebbers et al. (2000). Following the 24 -hour incubation, each Mason jar was capped tightly with a lid fitted with a rubber septum. A time zero CO 2 reading was taken immediately following capping, by injecting 0.5 mL of headspace into a LI -COR LI -820 infrared gas absorption analyzer (LI -COR Biosciences, Lincoln, NE). Three subsequent readings were taken over 90 minutes and a flux was calculated by regressing the change in CO 2 versus incubation period (Robertson et al., 1999). 171 Individual farmer meetings and qualitative analyses During the first part of each farmer meeting I asked farmers to describe each of their target fields including characteristics and challenges of each. Next, I presented both the MSU te st results (total SOM) and the active C results to the farmer. The last part of the meeting was unstructured, which allowed for more in depth questioning on farm history, management decisions, and soil testing. Each meeting took place in the winter following my sampling and lasted up to two hours. All meetings were recorded and notes were expanded within 24 hours of each meeting. Recordings were transcribed and analyzed for emerging t hemes and concepts by reading through transcriptions, writing text summaries, and coding transcripts within Nvivo 10.2 (QSR International, Burlington, MA). To compare test results to field observations and farmer experiences, I wrote summary memos of field characteristics and farmer descriptions and then constructed data matrix displays (Tables 6.3 -6.5) with extracted text combined with active C results to examine common concepts and themes. Statistics Paired t -tests were used to compare active C and total SOM results between different fields . I calculated the coefficient of variation (CV) to determine the variability (n=4; four fields per farm) of total SOM compared to active C . RESULTS Variability in total SOM and active C across farmer fields Averaging across the 13 farms, variability was lower for total SOM, with an average CV of 0.05 ± 0.01 compared to an average CV of 0.30 ± 0.03 for Active C between the Best and 172 Worst fields (Table 6.2 ). In addition, there was always more variability in active C between the other paire d fields compared to total SOM (Table 6.2). Differences between Bes t and Worst fields: total SOM versus a ctive C Total SOM was statistically similar in all row -crop field comparisons across the three counti es (Table 6.2) . SOM was slightly higher in the Best field, with a mean of 38.5 ± 0.4 g SOM kg soil -1, across the 13 fields compared to the Worst field (37.4 ± 0.3 g SOM kg soil -1), however, there were no significant differences between the two types of fie lds (t-test=2.8, p=0.0 7). Percent difference in total SOM between the Best and Worst fields ranged from 1.2 to 6.7 across the 13 different farms (Figure 6.3) . I found significant differences in active C between the Best and Worst fields (t -test= 5.8, p< 0.0001), where active C was greatest in t he Best fields with a mean of 43 ± 0.3 g C g-1 soil day -1 and significantly lower in the Worst fields (28.2 g C g-1 soil day -1). Percent difference in active C between the Best and Worst fields ranged from 1 3 to 60% (Figure 6.1 ). Penetration r esistance Penetration resistance for the subsurface 15 -46 cm depth interval was twice as high as the penetration resistance in the surface 0 -15 cm depth interval for the Best, Worst, C hoice, and NRC fields (Table 6.3 ). At the 0 -15 cm interval the Choice field (mean, 32.9 ± 4.4 psi) had significantly greater penetration resistance compared to the Best field (mean, 21.9 ± 4.9; t-test=-2.3; p=0.04). There was a marginally significant difference between the Best and the Worst fields (t -test= -2.0; p=0.06), where the Worst field had a mean of 30.1 ± 6.1. The Choice and Worst fields were statistically similar to one another (t -test= -0. 5; p=0.6). Among the different pairings at the subsurface 15 -46 cm interval, the Best (58 .5 ± 4.7) versus Worst (66.2 ± 5.2) field 173 comparison was the only pairing that showed significant differences from one another (t -test= -2.5; p=0.03). Field observations versus active C During the field observations and soil sampling, I measured compaction and took notes on soil structure and other field characteristics (6.4) . When describing the Best fields, I used - Worst fields in certain cases. In almost every case, my field observations of the Best field compared to the Worst field agreed with the active C test, which showed rates to be highest in the Best field (Table 6. 2). For example, I utilized phr ases su to describe the Best field at Farm No. 3. In contrast, I described the Worst field at Farm No. 3 as y, windswept, and rocky surface (see appendix). Farmer perceptions of soil health ver sus active C The characteristics that farmers used to describe their Best field were strikingly similar across the differe nt farms and counties (Table 6.5 ). Eleven out of the thirteen farmers mentioned describe the qualities of their Best field. Soil health and organic matter were also important characteristics for the Best field, as 70% of the farmers mentioned soil structure, quality, and/or soil nutrients. For example, farmer No. 7 organic matter, 174 Five farmers explained that consistent manure additions were what made a given field their best field, as illustrated by farmer No. 4, who explained differences on the production and last When describing the Worst field, all of the farmers mentioned something negative about soil quality. Farmer No. 13 s tated, , Yield or crop conditions were mentioned by 53% of the farmers but were often in conjunction with soil health indicators. For instance, farmer No. 5 described the Worst field as , is something i Farmer descriptions greatly contrasted between the Best and Worst fields, which mirrored results from the active C test, where the Best fields had greater active C than the Worst fields (Table 6.2 ). The importan ce of SOM and associated challenges There were several themes and concepts regarding SOM that emerged during the meetings with farmers. First, most farmers explicitly expressed the importance of SOM and all of the farmers mentioned challenges associated with building SOM. Second, farme rs noted different motivations for increasing SOM. Third, farmers reported a suite of management practices to address SOM, which were largely chosen based on cost. Finally, farmers connected soil test results to management strategies and expressed future i nterest in soil health testing. I described how SOM is related to ot her important soil properties; for example, farmer No. 6 stated , 175 The big thing is to get that organic matter up, because then, especially my soil, if you can get that organic matter up, then you could retain the moisture, then you can work with the fertil izers, then you got everything going for you, except the sunlight. And them soils, they'll produce, I've seen them produce, just as good as anything else, but everything's got to be right. The importance of SOM was further illustrated when farmers describ ed the time and effort it's not an overnight fix on that ground, we fixed up a lot of ground that has not been into farming over the years and we tried to build that ns with building SOM. At least two farmers stated that Farmer No. 1 state d, I pulled some of my tests in 2006 and 2000 and you look down through there, know Despite frustrations with building SOM, farmers described continuous efforts to improve SOM. I don't know how to fix it, I tried actually. What I've been doing, I'm trying to build organic matter, maybe I'm doing it the wrong way. It's been corn for 15 yea my organic matter 176 Motivations for building SOM Farmers offered different reasons for desiring greater SOM levels on their fields. Most farmers associated SOM with high yields. For example, farmer No. 5 stated, I mean organic However, nearly 50% went further and mentioned that it was part of their job. For example Farmer No. 6 you're supposed to take care of explained that farm success was dependent on healthy soils. Farmer No. 7 explains, ong time ago it's not what I'm growing above the ground, it's what I got going on below the ground. I mean, this is my Management practices used to build SOM The farmers in this study utili ze a wide variety of management practices to address SOM (Table 6.7). Every farmer incorporated crop rotations into their farm operation. Several farmers noted that adding wheat, alfalfa, or oats into a rotation helped to build SOM or humus. Farmers often qualified this thought by mentioning the root systems of rotational crops, for example, hat wheat, I think, you Cover crops were the second most common practice used to build SOM, but also posed the greatest challenges for farmers. Farmer No. 7 mentioned that cover crops brought his SOM up from 0.8%. Farmer No. 13 had used cover crops in the past and had great expectations for I want to see what these cover crops are going to do, because we can stop burning up th e carbon, we can start sequestering, and get those 177 from growing cover crops as he went on to explain that he would try a different type of radish cover in the upcoming year. When I asked what his expectation for the radish cover was, he said, Sixty -three percent of the farmers applied manure to increase SOM, but the amount and availability of manure varied for each farm. For example, some farms had cattle as part of their if you've got cattle you No. 5). In contrast, other farmers only had enough manure to add it to problem areas. Farmer No. 3 explained, If I could just get that sand[y] [spot] to grow something but I am [not getting crops to grow there]; it seems like I got cow manure from f armer No. 2 one year, smaller; [SOM] will raise some. Som e farmers mentioned that there are trade -offs associated with manure application, especially in terms of cost and compaction . As f I mean, I am phosphorus levels. 178 Nearly half the farmers in this study incorporated perennial crops such as alfalfa in their operation. The length of time that alfalfa was left to grow ranged from 4 to 12 years. Several farmers mentioned that the deep roots were the main benefit of the crop. For example, farmer No. 6 described how he has used alfal fa to improve soil quality over time, years and them spots aren't there no more. Forty -six percent of farmers actively used no -till as a strategy to increase SOM. Farmer No. 13 explained that building SOM was the main reason why he switched to no - if we can no- Only 30% o f the farmers mentioned residue management as part of their approach for building SOM. Farmer No. 7 was very adamant about keeping residues on certain fields, especially after wheat harvest, You got guys that come along to buy straw. I won't sell the str aw, I want to put was the biggest change [in SOM], the biggest help. than crop rotati ons or changing tillage practices. The least common approach for building SOM was utilizing probiotics and am endments I mean your organic matter is one of the most important things...and that's why we tried this Sumagrow¨, is it's your biological 179 they were only adding Sumagrow¨ spots and described a particular field where both soil quality and yield were down. He expl ained that he spent a large amount of money on gypsum in hopes of turning that field around, uple debt before that happens. Linking soil tests to management and expressed interest in soil health testing The final theme that emerged is that farmers mainly had a positive view of active C and its ability to aid in understanding SOM trends on their fields. An extension of this theme was that farmers raised important questions about active C and gave cr itical feedback that will be crucial for making soil health testing even more applicable in the future. When viewing the active C and total SOM results side by side, farmers immediately comprehended that the two tests were illustrating different tr ends across the fields. For example, farmer No. 12 said of active C i farmers were genuinely shocked by the results, for example the farmer who added gypsum to his field (Farmer No. 4) was surp rised when he saw significantly lower C fluxes compared to the , he goes on to say, 180 number [active C This farmer is frustrated because his problematic field (Choice field) and Best field have equal total SOM values. However, results from the active C test reveal that active C values are much lower in the Choice field compared to the Best field. Past total SOM test st a large sum of money into gypsum application. Several farmers had questions about the active C test. More than one farmer asked, W Farmer No. 1 questioned how active C could be useful if it varies, active C ] you know, can to know how they could raise active C rates in the Worst field to be on par with the Best field. At the end of each meeting, farmers were asked about the value of active C and soil health testing and if being a participant in the study was useful. All farmers expressed future interest in soil I think the more information that we all can get, it's something that we all need to improve the soils and to make it active C and other soil opinion, but other growers, this is wh ere MSU gets kicked in the you know what The real validation that farmers were interested in soil health testing is that twelve out of 181 DISCUSSION SOM is the most common indicator that farmers use to gauge soil health (Granastein and Bezdicek, 1992; Gruver and Weil, 2007). In this study, I combined quantitative field based research with field observations and meetings with farmers to determine i f two soil C indicators (total SOM and active C ) were able to detect differences amongst farmer fields and reflect farmer perceptions of SOM. The active C test proved to be more effective at detecting differences across farmer fields compared to the total SOM test. Furthermore, there were substantial differences in active C between the Best and Worst fields and more variation in active C between the different paired fields (Table 6.2). Active C also corresponded better to investigator field observations and farmer perceptions of soil health than did total SOM. Active C is a more sensitive test that reflects farmer experiences with yi eld and soil health and should be commercially available at soil testing facilities. Active C vs. SOM test results Variability in active C between the different paired fields was substantially greater than the variability in SOM, which indicates that the active C test was more capable of detecting differences among farmer fields. In particular, I found signific ant differences in active C between the Best and Worst fields, but found no significant differences in SOM. These findings concur with Culman et al. (2013) who also found significant differences in active C amongst different rotational crops and management practices but not in total soil C. Our findings contrast with Gruver and Weil (2007), however, who detected significant differences in total C between farmer chosen fields varying in soil quality. One explanation for this difference could be that Gruver a nd Weil (2007) used a combustion analyzer to detect total C (Islam and Weil, 1998), 182 which is more sensitive than the loss on ignition method that was conducted at the MSU Plant and Soil testing laboratory (Abella and Zimmer, 2007). Neither the active C t est nor the total SOM test found differences between the Best and Choice and the Worst and Choice fields (Table 6.2 ). This is likely because the Choice fields were more intermediate in performance as noted by farmer descriptions and reported in investigato r field notes (Tables 6.4 and 6.6) . For example, seven farmers classified the C hoice field as a problematic field, four farmers characterized it as a better performing field, and two farmers classified the Choice field as an average field (Table 6.6). The wide range of per formance amongst the different C hoice fields is reflected in the higher SEs, CVs and insignifican t t-test results between C hoice and Best and Choice and Worst field comparisons (Table 6.2) . Do active C test results support field observations and farmer perceptions? Results from the text summary analysis demonstrates that the active C test strongly supports field observations and farmer perceptions of soil health, especially when deciphering between the Best and Worst fields. In contrast, total SOM values were statistically similar across farmer fields and therefore did not support investigator field observations or farmer perceptions of SOM. Gruver and Weil (2007) also show that soil C indic ators strongly correlate with farmer perceptions of soil quality, however, in contrast to this study, they found that total C had just as strong a correlation with farmer perceptions as other labile soil C indicators. In this study, the lack of distinction between the Worst and Choice fields in field observations and farmer experience was also reflected in the active C test results, where results between the two fields were statistically similar (Table 6.2) . 183 Every farmer had previously submitted soil for total SOM testing either through MSU or a commercial laboratory. In addition, all farmers expressed knowledge of the importance of SOM and nearly half mentioned that SOM was the first indicator that they examined when receiving soil test results back. Farmers mentioned that SOM results were used to guide inorganic and manure fertilizer application and other important management decisions. Furthermore, over half the farmers mentioned that managing for SOM had important implications for the future of the ir farms. This sentiment is not unique to this study, as Kimble (2007) found that farmers across the United States are concerned about the environment and strive to improve soil health for the next generation of farmers. Furthermore, these findings illustr ate that farmers are utilizing a wide range of management practices that the total SOM test failed to detect. For example, the total SOM test often did not pick up differences between fields receiving heavy amounts of manure and fields that had not received manure in over twenty years. Given the level of importance that farmers place on SOM, it is problematic that the total SOM test results did not correlate with farmer perceptions of soil health. Meetings with farmers demonstrated that in some cases the lack of correlation between test results and farmer perception hinders appropriate management practices. Bridging the gap between scientific testing and farmer knowledge Farmer involvement in this study led to new understandings regarding the relationship between soil quality and soil testing. For example, an important theme that emerged from the meet ings is that farmers recognized the discrepancies between their perceptions and experiences of soil health and total SOM test results. Furthermore, farmers voiced dissatisfaction that soil health tests like active C are not commercially available. The dis connect between total SOM test results and farmer perceptions illustrates a consistent problem that occurs when scientific 184 assessment contrasts with farmer knowledge (Barrera -Bassols and Zinck, 2003). Ethnopedologists, who study and document farmer percep tions of soils and approaches to management, argue that farmer knowledge needs to be reflected in basic soil science research (Lamarque et al., 2008 ). Farmers have a wealth of knowledge regarding the physical, chemical, and biological aspects of their soil s, but this vast knowledge is rarely incorporated in agricultural research (McCallister et al., 1999). Ethnopedologists have made gains in linking farmer soil descriptions with soil surveys and classification, especially in indigenous communities; however , more farmer knowledge needs to be incorporated in soil fertility research worldwide (Barrera -Bassols and Zinck, 2003). The active C test is an attractive example of a scientific tool that can detect short-term changes in management that are undetectable b y total SOM and also reflects farmer perceptions of SOM . Creating a stronger link between farmer perceptions of SOM and soil testing could help farmers make more informed decisions on management that could lead to economic and environmental benefits. For instance, farmers in this study invested a large amount of time and money in a variety of management practices in hopes of increasing SOM. In certain cases, the total SOM test hindered farmers from adopting more economically viable practices. In addition, the active C test can be an important indicator of long -term soil C dynamics as well as agronomic performance (Culman, 2013). From an environmental standpoint, scientists and policymakers continuou sly encourage farmers to adopt best management p ractices for C sequestration on -farm to offset CO 2 emissions from agricultural systems (Jarecki and Lal, 2011). Farmers will be more likely to me et target C sequestration goals if active C or other tests that are sensitive to changes in management are more widely available. 185 Future directions While the active C test results can better reflect farmer perceptions compared to total SOM, soil s cientists need to work with extension educators to make active C more interpretable before it can be useful to farmers. During farmer meetings, farmers mentioned that active C was difficult to follow because of its dynamic nature in comparison to total SOM . This critique is important because other studies have illustrated that active C can change within a given growing season based on crop growth and fertilizer application (Culman et al., 2013). If samples are taken at different points during the growing se ason, it could be difficult to make informative comparisons from year to year. Thus, farmers should test for active C either in the spring before planting or in the fall after harvest. This recommendation is similar to with the Cornell soil health lab samp ling instructions, where farmers are encouraged to sample once in late fall (http://soilhealth.cals.cornell.edu/extension/test.htm#when ). Other farmers asked what the average a ctive C rates were for the county, across different soil types, and in different cropping systems. These types of aggregated results are not yet known for active C and will require further research. Overall, this study shows that farmers see value in the a ctive C test along with other soil health indicators and are interested in using soil health testing in the future. Finally, future research should explore how the active C test can be used to inform soil management plans and how to make the active C test more available and understandable to farmers. CONCLUSIONS Farmers depend on soil testing to make important management decisions that have consequences at both the local and global scales. Collecting and submitting samples requires time and money and therefore should reflect farmer perceptions of SOM and cha nges in 186 management. These findings demonstrate that total SOM, the principle soil C indicator used by farmers in the United States, is ineffective at separating best performing and worst performing fields. In contrast, I found that the active C test reflec ts significant differences across farmer fields and corroborated with investigator field observations and farmer perceptions. The qualitativ e analysis in this study revealed that every participant farmer was actively trying to maintain or increase SOM thro ugh a variety of different management pra ctices. In addition, farmers voiced frustration with the time required to build SOM. Even worse, some farmers were refraining from incorporating sustainable management practices because total SOM tests did not accur ately reflect the health of their soils. Active C can serve as a powerful tool for farmers that use SOM measures to make important management decisions and should therefore be widely offered at both university and commer cial soil testing laboratories. 187 APPENDIX 188 Table 6.1 Type and scale of participating farms in Michigan. Farm Michigan State County Farm size (hectare) Crops grown No. 1 Isabella 364 Corn, Soy, Wheat No. 2 Isabella 526 Corn, Soybeans, Oats No. 3 Isabella 324 Corn, Soy, Wheat, No. 4 Isabella 526 Corn, Soy, Alfalfa No. 5 Isabella 607 Corn, Soybeans, Wheat, Alfalfa No. 6 Presque Isle 789 Corn, Soybeans, Wheat No. 7 Presque Isle 304 Corn, Soybeans, Oats, Alfalfa No. 8 Presque Isle 809 Corn and Soybeans No. 9 Presque Isle 32 Strawberry No. 10 Van Buren 202 Corn and Alfalfa No. 11 Van Buren 486 Corn, Soybeans, Alfalfa No. 12 Van Buren 2023 Corn and Soybeans No. 13 Van Buren 202 Corn, Soybeans, Wheat 189 Table 6.2 Mean total SOM and a ctive C for all fields and paired t -tests and mean CVs for field comparison across 13 farm s in Michigan. Fields Total SOM Active C Field Comparisons Total SOM Active C Total SOM Active C g SOM kg -1 g C g -1 soil day -1 t-test t-test Coefficient of Variation Coefficient of Variation Best 38.6 ± 0.1 43.0 ± 0.3 Best vs. Worst 2.8 5.8** 0.05 ± 0.01 0.30 ± 0.03 Worst 37.0 ± 0.1 29.1 ± 0.2 Best vs. Choice 0.8 1.9 0.10 ± 0.02 0.43 ± 0.04 Choice 36.5 ± 0.3 30.8 ± 0.5 Worst vs. Choice 0.4 -0.3 0.11 ± 0.02 0.25 ± 0.03 NRC 47.0 ± 0.3 40.3 ± 0.4 Best vs. NRC -2.2* 0.8 0.20 ± 0.03 0.24 ± 0.03 Worst vs. NRC -2.3* -2.8** 0.20 ± 0.03 0.31 ± 0.04 P<0.05=*, P<0.01=** Choice vs. NRC -2.2* -1.4 0.21 ± 0.03 0.40 ± 0.04 190 Table 6.3 Penetrometer resistance (psi) in four fields from each farm (means !± SE) . Instances where areas in the field exceeded the Best Field Worst Field Choice Field Non-Row Crop Field Depth Depth Depth Depth Farm 0-15 cm 15-46 cm 0-15 cm 15-46 cm 0-15 cm 15-46 cm 0-15 cm 15-46 cm Resistance (psi) Resistance (psi) Resistance (psi) Resistance (psi) No. 1 12.2 (0.9) 50.3 (2.1) 19.3 (2.9) 64.2 (4.9) 18.2 (1.2) 58.5 (4.7) 27.0 (2.4) 60.0 (4.1) No. 2 19.0 (1.6) 43.8 (2.3) 19.0 (2.3) 74.5 (2.4) 19.2 (2.3) 37.1 (1.6) 15.9 (1.5) 40.8 (3.3) No. 3 18.2 (0.7) 42.0 (2.9) 7.2 (1.9) 37.0 (4.0) 23.0 (2.7) 55.5 (4.1) 8.5 (0.8) 27.5 (1.3) No. 4 16.9 (0.8) 56.1 (3.4) 22.5 (1.7) 56.5 (5.9) 18.0 (1.9) 59.1 (2.6) 12.7 (1.2) 34.7 (2.6) No. 5 13.3 (1.3) 53.5 (3.7) 27.5 (2.8) 64.5 (2.5) 53.3 (3.0) 69.0 (2.8) 36.0 (2.3) 74.5 (3.2) No. 6 26.1 (3.1) 59.8 (5.8) 24.5 (2.7) 55.5 (4.4) 16.6 (2.8) 65.8 (4.7) < 95.0 (5.0) < 100.0 (0.0) No. 7 43.5 (1.5) 82.5 (4.8) 30.1 (1.8) 95.0 (4.0) 79.5 (5.2) < 99.0 (1.0) 48.3 (2.7) < 87.0 (5.9) No. 8 13.3 (2.4) 70.5 (5.5) 52.5 (2.1) 85.8 (3.9) 43.5 (1.9) < 87.8 (3.9) 40.5 (2.7) < 83.3 (5.5) No. 9 68.0 (11.4) < 100.0 (0) < 95.6 (4.4) 47.0 (3.7) < 96.0 (2.2) 43.5 (3.9) 51.0 (9.4) < 100.0 (0) No. 10 13.5 (3.2) 43.8 (3.4) 18.2 (2.9) < 66.0 (5.1) 33.0 (1.5) 59.0 (4.5) 35.0 (2.8) < 64.5 (6.5) No. 11 19.3 (1.9) <62.5 (6.8) 22.4 (2.4) 67.5 (4.4) 29.5 (3.4) 56.5 (2.9) 30.0 (3.3) 84.5 (2.9) No. 12 6.0 (2.5) 52.9 (5.3) 26.2 (2.3) 47.3 (3.8) 26.0 (3.4) 52.2 (5.6) 38.5 (3.7) < 54.5 (2.9) No. 13 15.9 (2.9) 43.5 (2.8) 26.5 (3.7) 47.0 (3.7) 19.0 (1.6) 43.5 (3.9) 12.3 (1.2) 26.4 (1.9) Average 21.9 (4.8) 58.6 (4.7) 30.1 (6.1) 62.1 (4.6) 36.5 (7.0) 60.5 (4.8) 34.7 (6.3) 64.4 (7.3) 191 Table 6.4 Isabella, Presque Isle and Van Buren Counties. Fields Best Worst Choice Darker in color (7 Farms) Good soil structure (6 farms) Earthworm activity (5 farms) Poor soil structure (2 Farms) Poor soil structure (8 Farms) Sandier ground (5 Farms) Evidence of drainage issues; oxidation (4 Farms) Crusted surface (3 Farms Pale in color (2 Farms) Earthworm activity (5 Farms) Good soil structure (4 Farms) Evidence of drainage issues; oxidation (3 Farms) Adequate soil structure (2 Farms) Poor soil structure (2 Farms) 192 Table 6.5. Summary and frequency of farmer field descriptions for Best and Worst Fields Fields Field descriptions Best High yielding (11 /13 Farmers) Good soil structure (7/13 Farmers) Receives Manure (5/13 Farmers) Higher soil organic matter (3/13 Farmers) No disease presence (2/13 Farmers) Good drainage (2/13 Farmers) Worst Low soil organic matter (5 /13 Farmers) Poor soil structure or health (11/13 Farmers) Low Yielding (7/13 Farmers) Badly managed in the past (4/13 Farmers) Disease (1/13 Farmers) 193 Table 6.6 . Choice field farmer descriptions separated by performance (Good performing field, Intermediate field, P roblematic field) as described by farmer across 13 farms. Good Performing Field Intermediate Field Problematic Field 4 Farmers Higher yields Good soil structure and friendly to till Reliable field 2 Farmers High pH that locks up fertilizer Sandier areas 7 Farmers Lower yields Compaction problems Low soil organic matter Drainage issues 194 Table 6.7 Management practice thematic categories and selected examples of approaches that farmers use to build soil organic matter. Crop Rotation Cover crop Manure Perennials No-till Residue Management Products (Amendments) (13 Farmers) I got a lot more organic more roots there to hold everything. I'm sure there's a lot more earthworms... (No. 11) (9 Farmers) I want to see what these cover crops are going to do, because we can stop burning up the C, we can start sequestering, and get those numbers up a little bit (13). (7 Farmers) I get a little manure from a typically looking to build on all the organic (6 Farmers) alfalfa into it. And I left it almost four years the soil starting to coming around. It's chock full of roots. To me, that's what I them roots (6 Farmers) We switched to no-till, [so] we can build up that organic matter (farm 13). (4 Farmers) You got guys that come along to buy straw. I won't sell the straw, I want to put that straw back into the field. (No.6) (2 Farmers) I mean organic matter is very important in growing the crop...And that's why we tried this summagrow is it's your biological No.5) 195 Table 6.8 la, Presque Isle and Van Buren Counties. Isabella County Farm Best Field Worst Field Choice Field Active C (C mineralization ) trends No. 1 Cloddy, crusted, poor soil structure and darker in color. Structure is poor a nd lighter in color compared to Best field. Clear drainage problems (high levels of mottling). Best>Choice>Worst I noted differences in soil color between Best and Worst fields, which often reflects differences in SOM. Active C rates were substantially higher in the Best field and lowest in the Worst. The active C test results supported my field observations. No. 2 Earthworm activity, evidence of mycorrhizae, good soil structure and dark in color. Evidence of earthworm activity, poor soil structure, crusted at surface, pale soil color. Earthworm activity and better structure than worst field. Choice>Best>Worst I n oted that the C hoice and Best field had better soil structure than the Worst. The Choice field had slightly higher fluxes compared to the Best field. The Worst field had poor soil structure and was lighter in color, which is reflected in active C. 196 Table 6.8 ) No. 3!Earthworm activity, dark top soil, and good structure. !Sandy, windswept, and burnt parts of field, rocky surface. !Topography, poor structure, oxidation and potential drainage problems. !Best>Worst>Choice I observed large difference s in soil structure, color, compaction, and texture amongst the three fields. I found evidence for poor soil structure in the Choice and Worst fields. The active C test results strongly supported my field observations. !No.4!No-till field, soil structure is excellent and dark in color. A large amount of residue on field, evidence of earthworm activity. !Field varies in quality of soil, compacted, evidence of oxidation and drainage issues, decent structure. !Earthworms, crusty surface, adequate soil structure. !Best>Worst>Choice Differences in soil quality and structure were noted across the three fields. The Best field had better physical structure and darker topsoil compared to the Worst and Choice fields. My observations did no t reflect the large differences found in the Worst and Choice field. ! 197 Table 6.8 d) No.5!Crusty at surface, dark in color, sandier areas structure as well. !Surface crusting, weedy, topography, and drai nage issues. !Compacted, poor structure, high in clay content !Choice>Best>Worst My observation of dark topsoil in the Best field was reflected in the active C test, where the Best field had greater C fluxes compared to the Worst field. My observations did not correspond with the large C flux found in the Choice field, which could be caused from the established wheat system or the Sumagrow¨ that was added. ! 198 Table 6.8 d). Presque Isle Farm Best Field Worst Field Choice Field Active C (C mineralization ) trends No. 6 No-till field, residue, dark in color but sandier soil Hilly field, oxidation and drainage problems, sandy soil with several rocks Sandier than B est field, poor drainage, adequate structure Best>Worst>Choice I observed that the Best field had soils that were darker in color, which often reflects greater soil organic matter and was supported by the active C test results. There were no note -worthy differences in the Worst and Choice field observations. However, the test found that C fluxes in the Worst were greater than in the Choice. 199 Table 6.8 No. 7!Soil is dark and rich in color, has good structure, rocky in some areas, and minimal oxidation. !Evidence of earthworms, compacted, dark soil !Well aggregated, compacted, adequate structure, and soils darker in color. !Best>Worst=Choice I noted that soils w ere darker in co lor with good structure in the Best field. I noted similar characteristics in the Worst and Choice fields, but they were both more compacted. My observations aligned with the active C test results. !No. 8!Earthworm activity and good soil structure !Cracked dry surface with a large amount of weeds, rocky. Past 3 cm clay content seems higher and more compacted. !Crusted surface, darker soil and good structure, oxidation evidence for drainage problems. !Best>Worst=Choice My observations noted that the Best field had good soil structure and earthworm activity, while, both the Worst and Choice fields had cracked surfaces that had compaction issues. My observations were supported by the active C test. ! 200 Table 6.8 No. 9!Evidence of earthworms, good soil structure, less compaction than other fields !Extremely compacted, small amounts of oxidation and evidence of drainage problems !Earthworm activity, sandier soil, good soil structure, compaction !Best>Worst>Choice I noted that both the Best and Choice fields had good soil structure and evidence of earthworm activity, which is often associated with greater SOM. I also observed surface crusting in the Worst and Choice fields. Overall, my observations aligned with the active C results, especially between the Best and Worst fields. ! 201 Table 6.8 Van Buren Farm Best Field Worst Field Choice Field Active C (C mineralization ) trends No. 10 Sandy loam, Clear evidence of a rye cover crop, poorer soil structure Sloped field, poorer soil structure, some corn residue Wetter soil, soils darker in color, good aggregation Best>Choice>Worst I observed darker soils with better aggregation in the Choice field compared to the Best field. However, C fluxes were greater in the Best field. My observation of poor soil structure in the Worst field align with active C test results because the Worst field had the lowest C flux. No. 11 Higher in clay, strong soil structure, high in residue. Poor soil structure, earthw orm activity, sandier soils, some areas were darker with more clay, residue on field. Sandy but good soil structure, earthworm activity, and corn residue. Worst=Best=Choice C fluxes were extremely even across the three fields. The only noteworthy differen ces in my observation amongst the fields, was the poor soil structure in the Worst field. All fields were no -till with plenty of residue left on field. I found no difference in active C across the three fields. 202 Table 6.8 d) No. 12!Sandy loam, good aggregation, darker in color !Extremely sandy, some areas appeared to be nitrogen stressed, earthworm activity !Field extremely variable, some areas dominated by clay others by sand and gravel. !Best>Choice>Worst The Worst and Choice fields were sandie r and more variable compared to the Best field. The active C results supported my observations of the large differences in soil quality in the Best field compared to the Worst and Choice fields. !No. 13!No-till, sandy but good soil structure, corn residue !Poor soil structure and extremely sandy !No-till, dark sandy soil, a few grubs, lots of corn residue !Best>Worst>Choice I noted good soil structure in the Best field and poor soil structure in the Worst field, wh ich corresponds with the active C results. However, I only noted positive characteristics in the Choice field, which ended up having the lowest active C rates . ! 203 Table 6.9 Farmer descriptions and experiences of Best, Worst, and Choice fields and C flux trends in Isabella County. Isabella County Farme r Best Field Worst Field Choice Field Active C (C mineralization ) result trends No. 1 it's much more mallow than say the other I think it's heavier soil than the other one and it doesn't just doesn't work nearly as nice as the other one. SOM not as high as the [best] more loamy and Best>Choice>Worst The Worst field had the lowest C fluxes and clearly detected the lower soil organic matter described by the farmer . No. 2 Choice>Best>Worst The active C test detected the differences in management practices between the Choice and Worst fields. No.3 producing light soil and it's not heavy soil, it's in - there's a sandy ridge that was Best>Worst>Choice The Ch oice field had the lowest activ e C rate ; it could be that I did not sample the sandy ridge described by farmer. However, active C supported farmer perceptions of differences between Best and Worst fields. No. 4 the soil las tends to crust over in certain nice soil, as soon as it dries out, you can play Best>Worst>Choice The test detected differences between Best and Worst and detected lower C fluxes, possibly due to field compaction described by the farmer . 204 Table 6.9 d) No. 5! that's where the yields are really high and gets ! yielding ere is something in that ground and I don't ! supposed help with the !Choice>Best>Worst The extremely high flux in the Choice field could be due to the Sumagrow, which is designed to make C and N more available. In addition, the test detected difference s in soil quality between Best and Worst fields, described by the farmer. ! 205 Table 6.9 d). Presque Isle County Farme r Best Field Worst Field Choice Field Active C (C mineralization ) result trends No. 6 where all the manure got and fertility balanced to and pretty poor what we have in the That one's got the Problems include lower yields and water holding capacity due to sandy area Best>Worst>Choice Both the Worst and C hoice fields had problematic characteristics that caused lower active C rates . Thus, the active C test corroborated with farmer perceptions of soil health for each field. No. 7 and higher earthworm have less disea se pressure, it's always the farming practices prior to me taking tilled and over - Best>Worst=Choice The active C test detected the greater organ ic matter described by the farmer in the Best field and reflected the reduced soil health and lower SOM in the Worst and Choice fields. 206 Table 6.9 d). No. 8! Higher yields, higher SOM due to years of cattle grazing ! yields that we thought we were going to surprised that the soil organic matter is that high [farmer brought tests from previous ! !Best>Worst=Choice This farmer brought total SOM test results from previous years and was surprised that the total SOM was as high as it was in the Worst field because his yields have been lower then expected. The active C test detected a large difference between the Best and Worst field s and c orroborated with farmer perceptions of SOM .!No.9! incidents of black root ! compaction, and wet ! laying in between the !Best>Worst>Choice Important characteristics for a good field , as described by the farmer included no incidence of disease, while problematic fields were classified as compacted. While not always correlated, the higher active C in the Best field could be a reflection of an overall healthier system (no dise ase) compared to the Worst and Choice fields. ! 207 Table 6.9 d) Van Buren County Farme r Best Field Worst Field Choice Field Soil health (C flux) result trends No. 10 Yields were down, Compaction and drainage issues Best>Choice>Worst The C mineralization results aligned with farmer perceptions of lower SOM in the Worst field , as active C rates were lowest in the Worst field. No. 11 well -drained. It's got a variety of soils in it - clay, a little bit of muck on the for one, not flat like the and Worst=B est=Choice The farmer did not have major problems across the three fields. The differences that he noted were mainly in regards to soil texture. Here our test did not detect any difference across the three fields, which in many perceptions in terms of differences in soil C. 208 Table 6.9 d) No. 12! holding capacity, good ! Problems include: Sandy texture, soil fertility and difficulties increasing soil organic matter!Due to road construction in the 1950s, the field leveled all the field off, !Best>Choice>W orst The farmer mentioned that it was difficult to build soil C in the Worst and Choice fields. Our test detected large differences between the Best and Worst field, which closely aligns experience. !No. 13! field and the and field I can always count ! long ago and it was you can count on a 10 - organic matter ! !Best>Worst>Choice The farmer ment ioned lower SOM for the Worst field, which was reflected in the active C test. The Choice field had even lower active C than the Worst field, the farmer perceptions of SOM. The active C test corroborated with farmer perceptions Best field. ! 209 Figure 6.1 Percent difference for Total SOM and C mineralization between Best and Worst fields across 13 farmer field in Michigan. 210 REFERENCES 211 REFERENCES Abella, S. R., and B. W. Zimmer. 2007. 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