Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Impacts of land use on carbon storage and assimilation rates Ames, Susan Eveline 2000

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
831-ubc_2000-48596x.pdf [ 25.58MB ]
Metadata
JSON: 831-1.0089706.json
JSON-LD: 831-1.0089706-ld.json
RDF/XML (Pretty): 831-1.0089706-rdf.xml
RDF/JSON: 831-1.0089706-rdf.json
Turtle: 831-1.0089706-turtle.txt
N-Triples: 831-1.0089706-rdf-ntriples.txt
Original Record: 831-1.0089706-source.json
Full Text
831-1.0089706-fulltext.txt
Citation
831-1.0089706.ris

Full Text

IMPACTS OF LAND USE ON CARBON STORAGE AND ASSIMILATION RATES by SUSAN EVELINE AMES B.Sc. (Biology), Dalhousie University, Halifax, N.S.,1976 M.Sc. (Soil Science), University of British Columbia, 1981 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in  THE FACULTY OF GRADUATE STUDIES Resource Management and Environmental Studies We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA April, 2000 © Susan Eveline Ames, 2000  In  presenting  degree freely  at  this  the  available  copying  of  department publication  of  in  partial  fulfilment  of  the  University  of  British  Columbia,  I  agree  for  this or  thesis  reference  thesis by  this  for  his thesis  and  study.  scholarly  or for  her  I further  purposes  financial  gain  be  It  is  shall  that  agree  may  representatives.  requirements  not  that  the  for  Library  by  understood allowed  Department  of  Cjp . \  T h e U n i v e r s i t y o f British Vancouver, Canada  Date  DE-6  (2/88)  //)/, „  r  ^ / U A ^ C J Z Columbia  the that  without  permission.  ^,..~1L *v  advanced  shall  permission  granted  be  an  for head  make  it  extensive of  my  copying  or  my  written  ABSTRACT  A major contributor to global warming is the increasing amount of carbon dioxide in the atmosphere. Land use management may be a means to countering global warming by increasing the carbon sink potential. Terrestrial carbon budgets were prepared for forested (Douglas-fir), agricultural (hay), and urban sites in Abbotsford, B.C. The results indicate that the greatest amount of carbon is stored in the forested sites, followed by the hay sites, with the lowest amount in the urban sites. To maximize carbon in storage the use of trees either as forests or in pockets within the landscape is the best option. To simulate and to expand the utility of these carbon budgets, the study used the CENTURY model. The results of the simulations indicate that forests are a major carbon sink as was found earlier. Carbon storage under hay is at a relative steady state, except during the cultivation years when it becomes a carbon source. Lawn in an urban setting is a carbon source. The results of the simulations suggest that management can be used to increase the carbon sink. It also indicates that soils are a major carbon pool representing 20% of the forest, 90% of the hay, and 95% of the lawn budgets. For the general public and decision-makers to become more aware of the impact of changing land use on carbon storage, at the lot, local, or regional levels, they require a userfriendly decision-making tool. A derivative of the CENTURY model, CLU (for CENTURY Land Use), was developed. It was designed to be user-friendly and at the same time maintain the integrity of the parent model. It allows the user to input site-specific data and obtain site related output carbon data on a component basis, which can be used to assess how a potential  change in land use or management may affect the amount of carbon in storage. The model should be suitable as a research tool and for planning and educational purposes.  ABSTRACT ii LIST OF SYMBOLS AND ACRONYMS viii LIST OF TABLES ix LIST OF FIGURES xi LIST OF PHOTOS xiii ACKNOWLEDGEMENTS xiv 1 INTRODUCTION 1 2 MODEL SELECTION, STUDY AREA SELECTION, AND CARBON BUDGET DESIGN 11 2.1 Model Selection 11 2.1.1 Criteria 11 2.1.2 Model Description 13 2.2 Study Area Selection 16 2.2.1 Criteria 16 2.2.2 Study Area Selection 18 2.2.3 Study Area Description 18 2.3 CARBON BUDGET PARAMETERS 20 3 IMPACTS OF LAND USE ON CARBON STORAGE AND ASSIMILATION RATES IN ABBOTSFORD, B.C 25 3.1 INTRODUCTION 25 3.2 METHODOLOGY 26 3.2.1 Site Selection and Description 26 3.2.2 Carbon Analysis and Reporting 32 3.2.3 Data Collection 32 3.2.3.1 Forested Site 32 3.2.3.1.1 Trees 32 3.2.3.1.2 Understory 33 3.2.3.1.3 Coarse Woody Debris 33 3.2.3.1.4 Litter 33 3.2.3.2 Agriculture (Hay) 35 3.2.3.2.1 Hay 37 3.2.3.3 Urban Sites 38 3.2.3.3.1 Trees : 38 3.2.3.3.2 Lawn 38 3.2.3.3.3 Shrubs (Rhododendrons) 40 3.2.3.3.4 Annuals Plants (Geraniums) 45 3.2.3.4 Soils 46 3.2.3.5 Roots 49 3.2.3.5.1 Coarse and Small Roots 49 3.2.3.5.2 Fine Roots 49 3.3 RESULTS AND DISCUSSION 50 3.3.1 Forested Land Use 50 3.3.1.1 Species Composition 50 3.3.1.2 Age of Douglas-Fir Stands 53 3.3.1.3 Douglas-fir Trees - Carbon in Storage and NPP Aboveground 54 3.3.1.4 Understory 57 iv  3.3.1.5 Coarse Woody Debris 3.3.1.6 Litter 3.3.2 Agricultural Land Use 3.3.2.1 Hay 3.3.3 Urban Land Use 3.3.3.1 Lawn 3.3.3.2 Rhododendrons - Aboveground 3.3.3.3 Annuals 3.3.4 Roots 3.3.4.1 Trees 3.3.4.1.1 Coarse Roots 3.3.4.1.2 Small Roots 3.3.4.1.3 Fine Roots 3.3.4.2 Understory 3.3.4.3 Hay 3.3.4.4 Lawn 3.3.4.5 Rhododendrons 3.3.4.6 Annuals 3.3.5 Soils 3.3.5.1 Bulk Density 3.3.5.1.1 Forest 3.3.5.1.2 Agriculture (Hay) 3.3.5.1.3 Urban (Lawn) 3.3.5.2 Soil Carbon 3.3.5.2.1 Forest 3.3.5.2.2 Agriculture (Hay) 3.3.5.2.3 Urban 3.3.6 Land Use Versus Carbon Storage and Assimilation Rates - Summaries 3.3.6.1 Natural and Urban Forest Carbon Storage and NPP Budgets 3.3.6.2 Carbon Storage and NPP Budgets-Hay 3.3.6.3 Carbon Storage and NPP Budget-Lawn 3.3.6.4 Carbon Storage and NPP Budget - Rhododendrons 3.3.6.5 Annuals 3.3.6.6 Carbon Storage and NPP Budget - Forest, Agricultural (Hay), Urban 3.4 SUMMARY AND CONCLUSIONS 4 USE OF THE CENTURY MODEL TO ASSESS THE IMPACT OF LAND USE ON CARBON STORAGE AND ASSIMILATION RATES 4.1 INTRODUCTION 4.2 SELECTION OF THE DEFAULT DATA SETS 4.3 SIMULATION LENGTH 4.4 MODEL INPUTS AND SENSITIVITY ANALYSES 4.4.1.1 Latitude and Longitude 4.4.1.2 Climate 4.4.1.3 Natural and Urban Forests 4.4.1.3.1 NPP 4.4.1.3.2 Gross Primary Production  60 61 64 64 68 68 73 77 78 78 78 79 80 82 82 84 86 90 90 90 90 91 94 95 95 95 96 98 98 102 102 103 104 105 115 121 121 123 123 124 124 126 126 127 131  v  4.4.1.3.3 Carbon and Nitrogen in Storage 132 4.4.1.3.4 LeafArealndex 133 4.4.1.3.5 Coarse Woody Debris 136 4.4.1.3.6 Litter 137 4.4.1.4 Hay 137 4.4.1.4.1 NPP 137 4.4.1.4.2 Rotation 138 4.4.1.5 Lawn 139 4.4.1.5.1 NPP 139 4.4.1.5.2 Litter 139 4.4.1.5.3 Rotation 139 4.4.1.6 Soils 139 4.4.1.6.1 Soil Bulk Density 139 4.4.1.6.2 Soil Carbon 140 4.4.1.6.3 Soil Texture 142 4.4.1.6.4 Carbon:Nitrogen Ratio 143 4.4.1.6.5 Soil Drainage 144 4.5 RESULTS AND DISCUSSION 145 4.5.1 Natural Forest 145 4.5.1.1 NPP 146 4.5.1.2 Carbon in Storage 148 4.5.2 Urban Forest 156 4.5.2.1 NPP 156 4.5.2.2 Carbon in Storage 157 4.5.3 Hay 161 4.5.3.1 NPP 161 4.5.3.2 Carbon in Storage 163 4.5.4 Lawn 174 4.5.4.1 NPP 174 4.5.4.2 Carbon in Storage 176 4.5.5 Comparison of Simulated NPP of Natural Forest, Urban Forest, Hay and Lawn Land Uses 180 4.5.6 Comparison of Simulations - Carbon in Storage of Natural Forest, Urban Forest, Hay, and Lawn Land Uses 184 4.6 SUMMARY AND CONCLUSIONS 191 5 DERIVATIVE MODEL - CLU 196 5.1 Introduction 196 5.2 CENTURY Model Operation 197 5.3 CLU Operation 204 5.3.1 Inputs 211 5.3.2 Outputs 218 5.3.3 Data security and integrity 223 5.4 Summary and Conclusions 225 6 SUMMARY AND CONCLUSIONS 227 REFERENCES 239 Appendix A 256  vi  Description of CENTURY Model CENTURY MODEL Appendix B Leco Carbon Method Conversion - Biomass to Carbon Soil Legend Tipsy Analyses Parkinson & Allen Method Appendix C Sensitivity Analyses Model Inputs Simulation Output Appendix D Ecoregions CLU Definitions CLU Help  256 257 3.*Q a& 333. 33 S 53% 3 23 333 339 333  vii  LIST O F S Y M B O L S A N D A C R O N Y M S  AWHC  available water holding capacity  C  carbon  C0  2  carbon dioxide  C:N  carbon:nitrogen ratio  cm  centimetres  CWD  coarse woody debris  Db  soil bulk density  GPP  gross primary productivity  ha  hectare  m  metre  N  nitrogen  NEP  net ecosystem productivity  NPP  net primary productivity  P  phosphorus  PET  potential evapotranspiration  Ra  autotrophic respiration  R  heterotrophic respiraton  S  h  sulfur  viii  LIST O F T A B L E S  Table 1.1 Major carbon sinks and quantities  Page 4  Table 2.1 Study area biophysical characteristics  20  Table 3.1 Dimensions of rhododendrons  41  Table 3.2 Biomass (%) and NPP (%) component allocation for rhododendron shrubs  44  Table 3.3 Inventory of forest site vegetation  52  Table 3.4 Carbon in storage in forest litter  63  Table 3.5 Aboveground NPP carbon of hay  64  Table 3.6 Aboveground carbon in storage in hay  66  Table 3.7 Aboveground NPP carbon of lawn  68  Table 3.8 Aboveground carbon in storage in lawn  71  Table 3.9 Short- and long-term aboveground carbon in storage in rhododendrons  74  Table 3.10 Long-term aboveground carbon in storage in rhododendrons  74  Table 3.11 Aboveground NPP carbon in rhododendrons  76  Table 3.12 Carbon stored in tree fine roots  80  Table 3.13 Carbon stored in hay roots  83  Table 3.14 Carbon stored in lawn roots  84  Table 3.15 Carbon stored in rhododendron roots  87  Table 3.16 NPP of rhododendron roots  89  Table 3.17 Bulk density (Db) of forest and agricultural (hay) soils  91  Table 3.18 Bulk density (Db) of 10-cm cores versus 1.5-cm cores on agricultural (hay) soils  92  Table 3.19 Bulk density (Db) of 1.5-cm cores on agricultural (hay) soils 1996 vs. 1997  94  Table 3.20 Soil carbon in forests soils  95  Table 3.21 Soil carbon in agricultural (hay) soils  96  Table 3.22 Soil carbon in the urban soils  97  ix  Table 3.23 Carbon budget of natural forests  98  Table 3.24 Carbon budget of urban forests  99  Table 3.25 Carbon budget of land under hay production  102  Table 3.26 Carbon budget of land under lawn  103  Table 3.27 Carbon budget of 20-year-old rhododendrons  104  Table 3.28a Summary of carbon budgets - storage  106  Table 3.28b Summary of carbon budgets - NPP  114  Table 4.1 Hay rotation  138  Table 4.2 Comparison of simulated and estimated/observed NPP of a natural Douglas-fir forest and simulated NEP  146  Table 4.3 Simulated carbon budget of second growth Douglas-fir forest  148  Table 4.4 Simulated urban Douglas-fir forest NPP & NEP  157  Table 4.5 Simulated carbon budget of an urban forest  158  Table 4.6 Comparison of simulated and average observed NPP of hay crops and simulated NEP  162  Table 4.7 Simulated carbon budget of hay land use  164  Table 4.8 Lawn NPP simulated versus observed and simulated NEP  174  Table 4.9 Simulated carbon budget of lawn  177  Table 4.10 Summary of simulated carbon budgets - NPP & NEP  181  Table 4.11 Summary of simulated carbon budgets - storage (average of 20 years) Table 5.1 Steps to operate the CENTURY model  184 197  Table 5.2a Tree/Crop/Fix Options (drop down menus on "Profile" window)  213  Table 5.2b Location options (drop down menu on "Profile" window)  213  Table 5.3c Soil/Other Inputs (on "Profile" window)  213  Table 5.2d Management/ Scheduling Options (on "Schedule" window)  214  Table 5.2e Options for each management action (drop down menus on "Schedule" window)  214  LIST O F F I G U R E S  Page Figure 2.1 Carbon pools, by land use  21  Figure 3.1a Forest site locations/soils map, Abbotsford, B.C  27  Figure 3.1b Agricultural and urban site locations/soils map, Abbotsford, B.C  28  Figure 3.2 Average aboveground carbon in storage of rhododendrons in Abbotsford, B.C  75  Figure 3.3 Average aboveground NPP of rhododendrons in Abbotsford, B.C  77  Figure 3.4a Carbon in roots is agricultural (hay) soils, Abbotsford, B.C  109  Figure 3.4b Carbon in agricultural (hay) soils, Abbotsford, B.C  109  Figure 3.4c Carbon in roots is forest soils, Abbotsford, B.C  110  Figure 3.4d Carbon in forest soils, Abbotsford, B.C  110  Figure 4.1 CENTURY model flow chart  125  Figure 4.2 Relationship between leaf area index and tree production  136  Figure 4.3 Forest (2 growth Douglas-fir) - stemwood over time  149  Figure 4.4 Forest (2 growth Douglas-fir) - branches, leaves, and litter  150  Figure 4.5 Carbon flows of forest submodel  151  nd  nd  Figure 4.6 Forest (2 growth Douglas-fir) - fine/small root storage and NPP  153  Figure 4.7 Carbon flows of the CENTURY model  155  Figure 4.8 Urban forest - carbon in leaves and microbial pools  159  Figure 4.9 Soil carbon in slow pool - manured vs. chemically fertilized hay  172  Figure 5.1 CENTURY model - programs and file structure  198  nd  Figure 5.2 Beginning portion of a schedule file developed in EVENT. 100 file in the CENTURY model  199  Figure 5.3 Typical screen in preparing the schedule in the EVENT. 100 file in the CENTURY model  200  Figure 5.4 Example of an input screen in a Site. 100filein the CENTURY model  201  Figure 5.5 Example of input parameters presented in manual of the CENTURY model  203  Figure 5.6 Example of crop options in the File. 100fileunder "CROPS" in the CENTURY model  204  Figure 5.7 The "Welcome" screen in CLU  206  Figure 5.8 A "Profile" screen in CLU  207  Figure 5.9 A "Schedule" screen in CLU  208  Figure 5.10 List of output variables (forest)  209  Figure 5.1 la Options under "File" in the tool bar in CLU  209  Figure 5.11b Options under "Windows" in the tool bar in CLU  210  Figure 5.11c Options under "Help" in the tool bar in CLU  210  Figure 5.12 Locations of Ecoregions  212  Figure 5.13 Example of summary table of inputs for a run automatically generated in CLU  217  Figure 5.14 Typical view of graphical output generated in CLU  219  Figure 5.15 Graph and output variables  220  Figure 5.16 Output with scale adjustment  221  Figure 5.17 Commands under "File" tool box on graph/output variable screen  221  Figure 5.18 Typical view of a histogram option generated in CLU  222  Figure 5.19 Typical view of a data table automatically generated in CLU following a run  223  LIST O F P H O T O S  Page Photo 3.1 Urban park (Larch Park), Abbotsford BC Photo 3.2 View of residential lot, Abbotsford, BC  30 30  Photo 3.3 Forest litter sampling  36  Photo 3.4 Geraniums plants from Abbotsford, BC  36  xiii  ACKNOWLEDGEMENTS  I would like to thank my supervisor, Dr. Les Lavkulich, for giving me the opportunity to carry out this research, for his on-going support, insight, and guidance, for his support through his laboratory, and for his constructive comments on the drafts of this dissertation. I would also like to thank my supervisory committee, Drs. T.A. Black, M.C. Fortin, M. Garland, J.P. Kimmins, and M.D. Pitt for their support, enthusiasm, and comments on the on the drafts of my dissertation. Special thanks to Dr. William Parton for allowing me to proceed with CLU and to Clive Goodinson who carried out the programming of CLU - for his creativity, his enthusiasm, and his excellent programming. Special thanks to the individuals in the urban area who helped with my study and allowed me to sample on their property: Mr. and Mrs. W. Koberstein, Mr. and Mrs. W. Anderson, Mr. E. Kroeker, Mr. J. Blackburn, Ms. J. Konda-Witte, Mr. and Mr.s P. Hoogstraten, Mr. and Mrs. G. Rothney, Mr. and Mrs. R. Marcelus, Mr. and Mrs. H. Gasperdone, Mr. and Mrs. Egmond, Ms. J. De Wold, and Ms. L. Smith. Special thanks also to the farmers and owners of the forested sites for allowing me carry out my studies on their properties: Mr. A. Scheffler, Mr. M. Pastro, Mr. R. Blair, Mr. R. Morice and M. Kopelow, Mr. Weisskopf and the owner of the forested sites off of Cearbrook and Huntingdon Roads. Thank you to the various professors in Soil Science who offered their knowledge and insights and made my time at U.B.C. a good experience. I also want to thank the students in Soil Science and in Resource Management and Environmental Studies, and Alison Block and Martin Hilmer as they have been good friends and have supported me throughout my research. Thank you also to those who have helped me in my research along the way, including Jane Huang, Paul Leung, Yuka Ota, Keith Erin, Carol Dyck, and Karen Ferguson.  xiv  I also want to write a special thank you to my very good friends who have supported throughout my research. And last but not least, I want to dedicate this to two very special people in my life, my children, Charlotte and Alexander.  1  INTRODUCTION  The greenhouse effect, the heating of the earth's atmosphere as a result of greenhouse gases and water vapor that absorb infrared radiation (Mahlman, 1997; Clarke, 1992), has been accepted as a present reality and future threat (Mullet, 1997). Carbon dioxide ( c o 2 ) is considered the most important greenhouse gas (IGBP, 1998; Environment Canada, 1994) and is considered responsible for more than half of the greenhouse effect (BCMOE and BCMPR, 1995). Other gases such as methane and nitrous oxide are also important greenhouse gases. Although methane absorbs and re-radiates approximately 21 times as much heat energy as c o 2 , methane's contribution is lessened by its relatively short lifetime of 10 years as an atmospheric gas (Hengeveld, 1991; Rodhe et al, 1991). The effective lifetime of c o 2 is between 100 and 200 years (BCMOE and BCMPR, 1995). Nitrous oxide is about 206 times more effective than c o 2 as a greenhouse gas (Hengeveld, 1991). It's effect, however, is small in comparison to c o 2 as the amount added to the atmosphere is substantially less (Hengeveld, 1991). The contributions of other man-made compounds such as HFCs (hydrofluoro carbons), PFCs (perfluoro carbons), and SF6 (sulphur hexafluoride) are only a few percent and are being ameliorated by restricting their production (Bolin, 1998; Hengeveld, 1991). Thus, c o 2 remains the gas of major concern. The burning of fossil fuels with the resultant release of c o 2 is considered to be a major contributor to the greenhouse effect (IGBP, 1998; Houghton, 1995; Environment Canada, 1994). Anthropogenic emissions of c o 2 from fossil fuels have added approximately 1.5 x 10  11  metric tons of C to the earth's atmosphere in the last 100 years (Spencer, 1991) at 3.4 x 10  9  metric tons yr" (Eswaran et al, 1995). The increased c o 2 from burning fossil fuels is due to 1  1  the absence of it's own C gas 'sink' (in a decadal time scale versus a geological time scale) (IGBP, 1998). The clearing of forested land is also recognized as a major contributor to increasing c o 2 in the atmosphere through burning and the decomposition of the plant material remaining after clearing (Schmidt, 1998; Bouwman, 1995; Houghton, 1995). Deforestation currently results in approximately 20% of the c o 2 emissions worldwide (Schmidt, 1998). The annual release of c o 2 from the decay of humus held within forest soils in the world has been estimated at 2.0 x 10  12  metric tons C (Bramryd, 1983). Agricultural practices, such as cultivation, also are  considered to contribute to the greenhouse effect (Houghton et al, 1999; Buyanovsky and Wagner, 1995; Li, 1995; Kern and Johnson, 1993; Stewart, 1993; Mann, 1986). Cultivation exposes soil organic matter to microbial decomposition, which results in the release of c o 2 . It also exposes fresh topsoil to rapid drying which releases organic compounds through the breakdown of soil aggregates bound together by humic materials (Stewart, 1993). Paul et al. (1997) found that soils cultivated for 90 years had 35% less total organic C in the 0-10-cm depth and 21% less in the 10-20-cm and 20-30-cm depths compared to adjacent native soils. Agriculture contributes 21-25% of the anthropogenic emissions of c o 2 (Duxbury et al, 1993). Although vegetation and soils are sources of c o 2 , both are also very important C sinks (Schmidt, 1998; IGBP, 1998; Moffat, 1997; Sedjo, 1989). Terrestrial vegetation extracts about 1.2 x 10 metric tons of C from the atmosphere annually (Prentice, 1993). Total C in 11  phytomass on land is estimated at 1.2 x 10 metric tons dry weight with the majority, 82% or 12  9.8 x 10  11  metric tons, concentrated in forests (Rodin et al, 1972). Global soil C is estimated  to be between 1.4 and 1.7 x 10 metric tons (Houghton and Skole, 1990). Total C stored in 12  soils represents 2 to 3 times the amount of C present in the atmosphere as c o 2 (Royal Society of Canada, 1993) and in the vegetation (Houghton, 1995). There are approximately 2.4 x 10  9  2  metric tons of C stored in agricultural mineral soils in Canada (Royal Society of Canada, 1993). In agroecosystems, the major C pool (approximately 90%) is in the form of soil organic matter, followed by living phytomass (5%) which consists of crops, grasses, farm woodlots, and orchards (Royal Society of Canada, 1993). Are there other sinks? How do forests and soils compare to them? Oceans are recognized as net sinks for CO2 (Sarmiento and LeQuere, 1996; Sundquist, 1993). They contain approximately 3.6 x 10 metric tons C (Scurlock and Hall, 1991). Total C in 13  7  phytomass in the world's oceans is estimated at 8.5 x 10 metric tons, which is about 15,000 times less than that of land (Rodin et al, 1972). Total primary production of the world's oceans 10  is at 3.0 x 10  ,  metric tons C year , one third as much primary production as occurs in  terrestrial plant communities (Rodin et al, 1972). The capacity of oceans as sinks may be reduced as a result of global warming (Sarmiento and Le Quere, 1996). Peatlands are also large accumulators of C. The total amount of organic C accumulated 11  each year in the world's peatlands is estimated at 2.2 x 10 metric tons C, with the most extensive accumulations in the sub-arctic and boreal regions (Bramryd, 1983). Harvesting of peatlands has resulted in the release of large amounts of organic C and global warming may change the accumulation rate of organic C in these systems. Landfills can be regarded as long-term accumulators of C, and in this regard, can be compared with peatlands. In landfills, decomposition is slow and C is slowly released through microbial activity since the waste, at least in modern landfills, is compacted (Bramryd, 1983). This results in a decrease in aeration and therefore, in the decomposition rate. Bramryd (1983) found that one third of the organic C in a landfill was unmineralized after a period of 30 years and no significant difference in C concentrations was found between a 20- and a 30-year-old 3  landfill. Approximately 5.0 x 10 metric tons C in waste which is dumped annually into landfills will represent long-term accumulation and will be withdrawn from the C cycle (Bramryd, 1983). This compares to 2.2 x 10 metric tons C accumulated annually in the 11  world's peatlands (Bramryd, 1983). Table 1.1 is a summary of the major C sinks.  Table 1.1 Major carbon sinks and quantities. Sinks  Reference  Total Carbon (metric tons)  Carbon Accumulated Annually (metric tons yr" ) 1  Forest  Rodin et al, 1972  9.8 x 10" *  Soils  Houghton & Skole, 1990  1.4- 1.7 x 10  Oceans  Scurlock &Hall, 1991  3.6 x 10'  Peatlands  Bramryd, 1983  2.2 x 10"  Landfills  Bramryd, 1983  5.0 x 10  3  10  * Original data in phytomass; assumes phytomass is 50% C  A number of questions are on the international agenda: Can global warming be slowed or reversed? The reduction in fossil fuel burning is an obvious option in reducing the acceleration of global warming. As vegetation absorbs C O 2 during photosynthesis, can increasing growth be another option? Can soil C be increased? Are these valid options? Are they being considered? Reforestation and increasing forest production to counteract the greenhouse effect is being considered (Moffat, 1997; Kursten and Burschel, 1993; Rowntree and Nowak, 1991; Sedjo, 1989; Sedjo and Solomon, 1988). The management of agricultural systems has also been suggested as a means to mitigating global warming (Li, 1995; Cole et al, 1993; Kern and Johnson, 1993; Stewart, 1993). The potential of vegetation and soils to counter the greenhouse  4  effect and act as major C sinks is recognized internationally (Bolin, 1998; Kaiser, 1998). In the recent policy statement of the Third Conference of Parties to the Framework Convention on Climate Change in Kyoto, forests in particular have been identified as ameliorating agents of C O 2 increases (Bolin, 1998). Canada's National Action Program on Climate Change also recognizes the role of forest and agricultural management in reducing greenhouse gas emissions (Government of Canada, 1995). This is also recognized by federal and provincial ministries (Environment Canada, 1997a; BCMOE et al, 1995). Conversely, while these national and international policies are being developed, forested and agricultural lands are being lost to urbanization such as in the Lower Fraser Valley, B.C. (Boyle et al, 1997; Environment Canada et al, 1992; Moore, 1990). Local pressure for development in this region will likely continue as a result of the growing population of a nearby city, Vancouver. It is under these conditions that local management decisions can have an impact on global warming. The question then becomes: How can citizens, planners, and policy makers be convinced or at least become aware that vegetative surfaces do in fact ameliorate (or reduce) C O 2 levels in the atmosphere? Although the greenhouse effect is global, it is the result of a multitude of small events (Meyer and Turner II, 1992), such as the clearing of land for agriculture or the conversion of agricultural land for urban use? A scientifically credible tool such as a user-friendly computer modeling application which allows the stakeholders to assess the effects of land use activities at the local level on C storage and assimilation rates (net primary productivity [NPP]) may be one means to convince the stakeholders that land use can have an effect on atmospheric C. This computer tool should also be able to simulate a range of scenarios, which can be evaluated, and the results extrapolated to other regions. The development of such a tool is consistent with the priorities of the Canadian Climate Action fund (CCAF, 1999) - that is, support for the development of 5  techniques concerned with global warming issues which can be applied to regions other than those which they were originally developed. Therefore, the goal of this dissertation was to develop a tool, a computer application, to compare the effects of land use, forested, agricultural, and urban on terrestrial C storage and assimilation rates. One of the benefits of using a computer model is that it provides the opportunity to make predictions into the future without having to wait for the future to collect the data. A computer-based decision-making tool would allow the population, such as the residents of the Fraser Valley, to make judicious choices in land management, i.e., to manage land to achieve the greatest potential for C storage, even with increasing urbanization. The premise is that if people can be made aware of the potential effects of local land use changes on C storage, there is a greater likelihood that they may redirect their actions to maximizing it. This approach has been previously supported (CCAF, 1999; Couzin, 1999). It is one of the major premises of anagogy (adult learning), that adults adopt changes if they perceive a relevance to themselves (Griffith, 1994). If the impacts of land use changes on C can be assessed or predicted and communicated effectively at the residential lot level (a local scale), or on a regional basis, this could provide the relevance to stakeholders. It would provide the stakeholders with the potential to affect their local actions and contribute to the amelioration of a global issue (CCAF, 1999; Couzin, 1999). As noted by Danielson (1993), every tonne of C0 removed 2  from the atmosphere by photosynthesis is considered a gain in controlling the greenhouse effect. Therefore, the first objective of this dissertation was to select an appropriate scientific model to use for simulating C storage and release for forested, agricultural, and urban land uses. The CENTURY model was selected. It has the potential to provide C values for components of forested and agricultural systems and thus C budgets can be prepared from these 6  by compiling the different components of each system. The criteria used to select CENTURY are described in Chapter 2. Carbon accounting or C budget development is a very active area of scientific inquiry particularly in the forested area (Yang, 1998; Kurz et al, 1995; Harmon et al, 1990b; Gower and Grier, 1989; Cropper and Ewel, 1984; Keyes and Grier, 1981; Turner and Long, 1975; Cole et al, 1967). Carbon dynamics of agricultural systems have also been assessed (Li, 1995; Cole et al, 1993; Stewart, 1993; Parton et al, 1987; Mann, 1986). Research in C budget development in urban systems is limited. In urban areas, trees have been assessed as a means to countering global warming (McPherson, 1994; McPherson et al, 1993; Rowntree and Nowak, 1991) as well as lawn (Falk, 1980; Falk, 1976; Madison, 1971). These studies have been carried out using different methodology. As well, there is no standard or common list of parameters that make up the budgets. In addition, the data has been collected on localized sites in different ecoregions such that true comparisons cannot be made as climate and site factors, which greatly influence C assimilation and storage, have not been held constant. To make meaningful comparisons among the three land uses consistency in methodology, component (C pool) selection, and site factors is required. Thus, this research involved afieldprogram in which all land uses occurred in the same ecoregion, using the same methodology with data collection on the same set of parameters. This provided the scientific evidence of the effects of land use on biomass production and C in storage. The second objective, then, was to select a study area in which to carry out thefieldprogram. The study area selected and the criteria used for the selection are described in Chapter 2. The third objective was to collect data for the C budgets and test the model. Not every aspect of the model's capabilities is required for this dissertation. Therefore, not every input or  7  output parameter available in the model was used. A list of the parameters requiring data for the carbon budgets is presented in Chapter 2. The C budgets developed for each land use are discussed in Chapter 3. The urban area was a particularly challenging system in which to develop a C budget. Urban systems can be very complex with pockets of trees in lawn spaces intermixed with areas allocated to shrubs and annuals. How can such a complex system be compared to forested and agricultural systems? The urban system was approached by considering it a mosaic of forested and agricultural land uses. The preparation of C budgets for urban systems was also challenging not only because there is little existing information or sampling methodology designed for estimating C fluxes in urban ecosystems, but also, sampling in an urban area is constrained by the fact that disturbance must be kept to a minimum as it occurs on residential lots. How these constraints and deficiencies in preparing carbon budgets in an urban ecosystem have been accommodated, as well as the other components of the field program, are discussed in Chapter 3. The literature as a data source was relied on as a supplement to the field program, particularly in the forested system. For the literature to be used in this type of research the values ultimately adopted require revision to reflect the study area conditions as much as possible. Critical evaluation of such existing information is an important part of C budget development. The literature cited and the manner in which the data were adapted to the study area are also discussed in Chapter 3. With the data collected for each component, the C budgets could be prepared. Each component of the C budgets is discussed in Chapter 3. The components were then compiled to produce the budgets, which could be compared. The trends and relative differences between  8  the three land uses are discussed. As well, the various sizes of the C pools of each type of ecosystem are discussed which indicate the parameters of greatest significance. The data used to prepare the budgets were then used as the input data to run the CENTURY model. The fourth objective was to test the results of the model simulations against the data collected in thefieldprogram and from the literature. Several questions arose during the testing. How sensitive is the model to the input ranges occurring in the study area? What would be the effect of doubling the value of a parameter, for example? How accurate do the various input values have to be? To answer these questions, sensitivity analyses were carried out. They are discussed in Chapter 4. The CENTURY model has not been previously used to simulate urban land use. Adjustments were made to accommodate trees and lawn in the urban setting and these are discussed in Chapter 4. One of the benefits of using a model for this purpose is that it can indicate trends over time. This is very important in terms of the C sink/source issue as this has implications in global warming (Houghton et al, 1999; Goulden et al, 1998; Moffat, 1997; Houghton, 1995; Kurz and Apps, 1995; Mackenzie, 1994; Heath et al, 1993). The three land uses were therefore assessed whether they are net C sources or sinks. This was done by examining the trends in changes of the various C pools from one year to the next over the period of the simulation. CENTURY also has the capacity to generate a parameter called decomposition respiration which represents microbial or heterotrophic respiration. With the availability of this parameter, an estimate could also be made on the net ecosystem productivity (NEP), which is the net amount of carbon assimilated annually by a system. In Chapter 4, just as in Chapter 3, each component of each of the budgets was discussed separately, then compiled to produce C budgets for comparison and discussion. Based on the 9  results of the simulations, the CENTURY model was considered to be a useful tool for comparing the effects of land use (forested, agricultural, and urban [trees and lawn]) on C storage and assimilation rates. The CENTURY model is not user-friendly and as such, is not suitable to be used by non-scientists as a decision-making tool for planning purposes. Therefore, revision of the model was required. The fifth objective was to integrate this scientific model into a userfriendly decision-making tool such that community planners and other practicing professionals can use it and demonstrate it to the public. It is recognized that for a model to be useful as a management tool at the local or regional levels, it is important that it does not require a team of specialists to operate it (Moon et al, 1995). Therefore, a derivative of the CENTURY model was required to make it available to such a community. This was carried out and is described in Chapter 5. As part of this effort towards user-friendliness, interfaces were developed which relied on the use of colour and graphics. Copies of these are included in this chapter as well as samples of output. The derived model is named CLU for CENTURY Land Use - a 'CLU' to the future. The summary and conclusions are presented in Chapter 6.  2  MODEL SELECTION, STUDY A R E A SELECTION, AND CARBON BUDGET  2.1  2.1.1  DESIGN  Model Selection  Criteria  Specific criteria were used for selecting the appropriate model. In this study, a forested, an agricultural, and an urban ecosystem are compared, at the local level. Ecosystems are complex and biophysical models based on ecosystem processes are viewed as powerful tools for predictive purposes (Cooper, 1969). Therefore, one of the criteria used was that the model be a process model. The terrestrial ecosystem model (TEM) (Melillo et al., 1993) is an example of a process based model. This model describes ecosystem processes, such as photosynthesis, respiration, decomposition, and nutrient cycling, and the effects of their interactions on C fluxes at the global level. Another term for this type of model is a terrestrial biogeochemical model (TBM). The TEM and TBM models have been developed for global simulations and thus do not have the capacity to simulate local conditions. This study is focused at the intermediate scale between individual plant species and global estimates. Focus on an individual plant does not lend itself to the spatial analysis required by communities and regions, while global models lose their capacity to simulate local conditions by the necessary simplification of the data. It was required that the model be a process model but also that the output be at an appropriate scale to accommodate local planning. This would provide the capability of scaling up and allows regional predictions to be made through an hierarchical approach.  11  Models are frequently designed for specific uses. For example, the FORECAST model (Kimmins et al, 1999) is a forest stand management model. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS) simulates forest growth (Kurz and Apps, 1995). The DSSAT group of models is designed for specific agricultural crops such as corn, soy beans, and peanuts (personal communication with Dr. Marie-Claude Fortin). All of these models may be suitable for this study for the specific ecosystem for which they have been designed. However, it is desirable for this project that the model be able to accommodate both forest and agricultural land uses (and be flexible enough to be adjusted for urban land use). To make meaningful comparisons between land uses, it was also essential that the model accommodate above- and below- ground components, including soil. The capacity to accommodate site-specific biophysical input data was also required as a means to compare the effects of land use at the local level on carbon storage and assimilation rates. A model could fit all of the above criteria but still remain unsuitable. For example, models can be so cumbersome that they are unusable or are so limited that the simulations are not acceptable (Kimmins et al, 1990). These properties can be inferred from the model's acceptance or frequency of use in the literature. That is, if a model is widely used and many references are made to it, preliminary assumptions can be made concerning its functionality. These factors were also considered in selecting the model. The acceptability of the underlying premises of a model was also considered. This can be assessed through validation exercises. Thus, the amount of validation a model had undergone was also a consideration in the selection process. The selected model should be flexible in order to simulate an urban land use. The flexibility criterion is a less obvious parameter to assess. A model used by a wide range of  12  researchers for a variety of purposes can be considered as an indication of flexibility. Therefore, when the literature was reviewed this factor was also considered. The CENTURY model met these criteria. 2.1.2  Model Description  The CENTURY model is a general FORTRAN model originally developed as a soil organic matter model, by Parton et al. (1987). It can be run on a regular personal computer. It simulates change in C storage and net primary production (NPP) for a variety of management scenarios. This model has been revised on an on-going basis. Agroecosystem Version 4.0 was used for this project, as described by Metherell et al. (1993). The model is described in detail in the support manual (Metherell et al, 1993). The structure of an earlier version is described in Parton et al. (1987). The CENTURY model is particularly suited for this project because it has the capacity to produce C budgets for the forest and agricultural ecosystems, including the soils. This is particularly important as the assumptions and algorithms used in models vary depending on the designer. With both ecosystems accommodated in the same model, this issue was removed. CENTURY is also a biophysical process model, which is required to accommodate the complexity of the ecosystems. Agroecosystem Version 4.0 can simulate C dynamics for a wide range of ecosystems and integrates the effects of the climate and soil driving variables. It is comprised of several submodels which include a soil organic matter submodel, a grassland/crop submodel, a forest submodel, and a soil water budget submodel (Appendix A, Figures A.l, A.2, A.3, and A.4, respectively). The CENTURY model also has nitrogen (N), phosphorus (P), and sulphur (S) submodels (Appendix A, Figures A.5, A.6, A.7 respectively). The model is described in more  13  detail in Appendix A. For example, the operation of the forest, water, and nitrogen submodels is discussed. A flow chart illustrating the sequencing of events and processes of CENTURY is also included in Appendix A. The CENTURY model accommodates the different ecosystems by providing default data sets from various ecoregions. At the same time, it allows the user to input as much sitespecific data as available. To produce site-specific results for local planning purposes, the model requires the capacity for input of site-specific data. The default data sets included with the model originate from research units across North America and thus cover a range of crop/tree types and biophysical areas. The object is to select the most appropriate data set, in terms of crop/tree and biophysical region and to run the model using these as the baseline data sets and input where possible site-specific data. The output data of the CENTURY model are presented on a square metre basis. This is a particularly suitable scale for residential lots as the vegetated area is frequently a mosaic of a variety of types of landscape materials and treatments that can change over a small physical space. Since each land use under consideration has a variable areal distribution, a unit of one metre square can be used for comparative purposes with the option to aggregate the data into larger spatial units. The CENTURY model has been applied to agricultural and forested ecosystems (Jacques Whitford Environmental Ltd. and U. Sask., 1999; Patwardhan et al, 1995; Voroney and Angers, 1995; Ojima et al, 1993; Peng et al, in press [a]). However, it has not been previously used to simulate urban ecosystems. The extensive use of the model for a range of research suggests that the model is flexible- that it has flexibility such that adjustments could be made to accommodate urban systems. For this study, it was adapted to urban land use by  14  considerating it as consisting of an agricultural component (lawn), and a forest component (trees). The frequent references to the model in the literature for a range of uses suggests that it is not only flexible but usable by non-programmers. As well, it suggests that the basis of the model is acceptable. Agren et al. (1991), in their review of the state-of-the-art models of forests and grasslands, describe the CENTURY model as simple enough to predict regional trends in the ecosystem processes, and mechanistic enough to explore regional responses to climate change. The capacity of CENTURY to accommodate ecosystem C dynamics allows it to simulate changes in net C fluxes for a given location (Smith et al, 1993). Jacques Whitford Environment Ltd. and U. Sask. (1999), in a review of several process models for measuring C stocks in agricultural systems, note the overriding advantage of the CENTURY model is its international acceptability, the large amount of research that has gone into its development, and its success in modeling prairie and grassland agro-ecosystems. The CENTURY model output of the boreal forest more successfullyfitthe observed data than other models tested by Peng et al. (in press [b]). Validation of the CENTURY model has been taking place since its early development. This has been carried out for grassland systems (Parton, 1984; Parton et al., 1987; Parton et al, 1988), and for its capacity to simulate the effects of climate change on grasslands (Ojima et al, 1993; Cole et al, 1993; Patwardhan et al, 1995; Burke et al, 1991). CENTURY has been successfully used to simulate regional patterns of plant production, soil organic matter levels, and other ecosystem properties for the United States Great Plains region (Agren et al, 1991). The CENTURY model has also been used to assess tillage and manure application effects on soil organic matter in eastern Canada (Voroney and Angers, 1995). The study indicated that the basic structure of the model was robust enough to accurately simulate the effects of 15  management on short-term changes in soil organic matter. It was used to assess tillage and crop effects on soil organic matter in eastern U.S.A. (Donigian et al, 1995). In that study, CENTURY was scaled up to produce regional simulations. CENTURY has also been used to evaluate the effects of soil texture, climate, and cultivation on soil organic matter in the Great Plains area of the U.S.A. (Burke et al, 1989). The output of the model generally closely compared to the C levels for the rangeland soils studied. The model was used to simulate the effects of organic amendments on soil organic matter in Sweden (Paustian et al, 1992). The results generally agreed "reasonably" well with thefieldexperimental data. Therefore, it was concluded that the main functional relationships of the model were supported. It has also been used to assess the impact of fertilizer additions and residue management on grain yield and soil quality in Oregon, U.S.A. (Parton and Rasmussen, 1994). A comparison between the simulated and observed data indicates that the model can predict soil C within +-5%, 57% of the time and grain and straw yield within +-10%, 70%) and 57%, respectively. The CENTURY model was revised for regional scale modeling of ecosystem interactions with climate change effects in the Great Plains, U.S.A. (Schimel et al, 1990). It has also been used for regional studies with geographical information systems (GIS) to simulate regional soil C storage (Jackson et al, 1995). 2.2  2.2.1  Study Area Selection  Criteria In a study in which C assimilation is the parameter of interest, the biophysical  environment is a major consideration. Carbon assimilation is affected by climatic factors such as temperature, precipitation, and photosynthetically active radiation (PAR) (Melillo et al, 1993; Chang, 1971). For example, plants photosynthesize over a range of temperatures with 16  photosynthesis decreasing at either extreme of temperatures (Yang, 1998; Teskey et al, 1995; Waring and Schlesinger, 1994). Higher temperatures cause an increase in the biochemical process by which CO2 is converted to carbohydrates but as well, can result in an increase in respiration and, thus, the release of CO2 (Chang, 1971). Water deficits can also retard photosynthesis as reduced conductance of water can limit diffusion of CO2 into the leaf as the stomata close (Teskey et al, 1995). Plant water stress also directly reduces the photosynthetic process through the dehydration of the protoplasm of the plant (Chang, 1971). Although plant species differ in their ability to withstand a water shortage before photosynthesis is seriously reduced, photosynthesis generally ceases when 60% of leaf moisture is lost (Chang, 1971). Therefore, to reduce variability as a result of climatic effects, the study sites in a field program should occur in the same biogeoclimatic zone. The rate of C assimilation is also affected by soil factors such as texture, drainage, depth, and soil organic matter content (Stewart, 1993; Brady, 1990; Parton et al, 1987). Therefore, the study sites should also occur on the same or similar soils to reduce soil-based influences. That is, the study sites in a region should not be selected randomly but by stratified random sampling (Pitt and Schwab, 1988). In other words, the region should be first stratified by selecting a particular soil type. The sites are then randomly selected on these soils. Decisions as to what degree of initial heterogeneity among the experimental units is permissible or desirable and the extent to which the regulation of the environmental conditions during the experiments should be attempted are a matter of subjective judgment (Hurlbert, 1984). These decisions, however, will affect the magnitude of the random error and therefore the sensitivity of the experiment (Hurlbert, 1984). Therefore, to reduce the influences of non-land use effects it was concluded that a comparison among ecosystems required that the ecosystems occur in the same biophysical environment, on the same soils, and under the same climatic conditions. 17  They should occur in the same topographic position in order to reduce the effects of differential slope and aspect. 2.2.2  Study Area Selection  Abbotsford, B.C. (latitude 49°5', longitude 122°32') was selected as the study area. It met the criteria in terms of land use, climate records, and soils. It is located in a rural area in the lower Fraser Valley undergoing rapid growth and contains the three types of land uses to be investigated in this study: forested, agricultural, and urban. Long-term climate data are available from the records collected by Environment Canada at the local airport climate station, which has been in operation for 40 years. This is important as the CENTURY model allows for the input of site-specific climate data. Thirdly, soil maps (scale: 1:25,000) are available that are detailed and fairly recent (Luttmerding, 1981). 2.2.3  Study Area Description  The Abbotsford area was predominantly under forest cover until the early 1930's, at which time clearing was initiated to accommodate agriculture (Luttmerding, 1981). It is located in the Coastal Western Hemlock biogeoclimatic zone. The soils for the field investigation were selected for their uniformity. They are mapped as a cartographic complex of Abbotsford and Marble Hill Soils (Luttmerding, 1981). These soils are developed on a medium textured (silt loam) aeolian (wind blown) capping overlying a very gravelly, very coarse textured, glaciofluvial deposit. Being developed from aeolian material, the soils are generally uniform in their chemical and physical properties, and by nature of their mode of deposition are non-gravelly. Coarse fragments affect sampling and soil volume.  18  These soils have the same moisture and nutrient holding capacities, factors that affect biomass production and, thus, C storage. They are well-drained by nature of the eolian capping and the underlying rapidly drained glaciofluvial material. These soils differ essentially in the depth of the aeolian capping. The chemical and physical properties of the soils are ideal for a wide range of uses, including forestry, agriculture, and urban. Previous studies of this area have reported growth of Douglas-fir (Pseudotsuga menziesii [Mirb.], Franco) at between 7.5 and 9 m ha" yr" of 3  1  1  merchantable wood (Luttmerding, 1981). As such, they have a forest capability rating of Class 1 (CLI, 1967). Lands rated as Class 1 have no important limitations to the growth of commercial forests (CLI, 1967). It is also a positive aspect of the study area that Douglas-fir trees commonly occur. This is an important tree in the Pacific Northwest. Thus, much data are available on this tree species. The availability of existing data on some components of the study was a major consideration as destructive sampling, particularly of the trees, was not permitted. The soils have a high agricultural capability rating. They are rated as a complex of agricultural capability Classes 2 and 3, with aridity and topographic limitations. The aridity limitation is based on a climatic moisture deficit. Soils rated as Class 2 have minor limitations for agricultural production and should be capable of supporting a wide range of crops with relatively high yields (BCMAF and BCMOE, 1983). Land rated as Class 3 has limitations that require moderately intensive management practices or moderately restrict the range of crops or both. All of the experimental sites occurred on level topography. Thus, these soils belong to the Class 2 component of the mapping unit with an aridity limitation. The study area has a mild climate. It has a relatively long frost free period of 169 days and a climatic moisture deficit which is relatively low at 98 mm (BCMOE, 1978). The 19  summers are moderate with a mean maximum temperature of 23° C in July and August. The winters are mild with the mean minimum temperature occurring in December and January at approximately at 4.5 °C (Atmospheric Environment Service, 1980). This climate is very suitable for forest and agricultural production. It has a Class 1 thermal rating and a Class 2 moisture deficit which can be countered with irrigation (BCMOE, 1978). This information is summarized in Table 2.1. Table 2.1 Study area biophysical characteristics. Description  Parameter  Selected Soils Abbotsford Soils (AB)  Marble Hill Soils (MH)  20-50 cm of medium textured, aeolian material overlying gravely, coarse textured, glaciofluvial outwash material; high moisture holding capacity, high nutrient holding capacity; moderate hydraulic conductivity; moderate cation exchange capacity; well drained; Orthic Humo-Ferric Podzols (Luttmerding, 1981). >50 cm of medium textured, aeolian material overlying gravely, coarse textured, glaciofluvial outwash material; high moisture holding capacity, high nutrient holding capacity; moderate hydraulic conductivity; moderate cation exchange capacity; well drained; Orthic Humo-Ferric Podzols (Luttmerding, 1981).  60% Class 2 (topographic and aridity limitations); 40%> Class 3 (topographic and aridity limitations). Forest Capability Class 1 Coastal Western Hemlock Biogeoclimatic Zone Frost free period: 169 days; effective growing degree days (EGDD) above 5° Climate C: 976; climatic moisture deficiit*: 98 mm (BCMOE, 1978); growing season (May to September) precipitation: 330 mm; annual precipitation: 1533 mm; average monthly maximum temperature: 23° C (July); average monthly minimum temperature: -1.5° C (January); mean daily temperature 4.4 °C (Atmospheric Environment Service, 1980). growing season evapotranspiration minus growing season precipitation (BCMOE, 1978).  Agricultural Capability  2.3  CARBON  BUDGET  PARAMETERS  The third part of this chapter concerns the selection of the parameters on which to collect data to develop the C budgets. The C budgets were first prepared based on data collected during the field program and supplemented with data collected from the relevant  20  literature. The value for a parameter collected in this first part of the study was then used to run the CENTURY model. The selection of the parameters which make up the C budgets for the three types of land uses requires a broad understanding of these ecosystems. The list of parameters for which data were collected included the aboveground and belowground plant components and soils (Figure 2.1).  CARBON POOLS BY LAND USE <^^gricultunT^>  Aboveground tree Aboveground understory Coarse woody debris Litter Tree coarse roots Tree small roots Tree fine roots Understory roots Soils  Aboveground crop Fine roots Soils  Aboveground tree Tree coarse roots Tree small roots Tree fine roots Aboveground shrub Shrub coarse roots Shrub fine roots Aboveground lawn Lawn fine roots Aboveground annuals Annual fine roots Soils  Figure 2 . 1 Carbon pools, by land use.  The number of pools per land use varies. This is a reflection of the amount of variability in the respective systems. A preliminary on-site investigation indicated that a wide range of agricultural uses, including hay production, occur in the Abbotsford area. Hay is  21  commonly grown in the study area so hay production was used to represent the agricultural land use. Hay offers a useful comparison to turf grasses as both are perennial and grass-based. Turf grasses differ from hay grasses as they have been adapted from grasses selected to withstand frequent cutting (Madison, 1971). They differ from hay and pasture grasses in that the apical meristem is located at or near the ground surface, below the point of defoliation (Turgeon, 1996). Leaf formation continues in turf grasses after each defoliation and so they can withstand frequent mowing and heavy traffic. The budgets for the hay and turf grasses are relatively simple in that they represent a monocropping system. They include the aboveground portion of the grasses, the roots, and the soil. Forests are complex ecosystems and when unmanaged can contain a mix of tree species at different successional stages and be accompanied by a wide range of understory vegetation, litter, coarse woody debris (CWD). Trees in a forested system represent the largest C storage pool and assimilator of C (Gower and Grier, 1989; Turner et al, 1975; Cole et al, 1967). Carbon in trees is stored in the leaves, branches, stemwood, and fine (<2 mm in diameter), small (2-5 mm in diameter), and coarse roots (>5 mm in diameter). Understory does not represent a major C storage pool. For example, understory represented 3% of total C in storage in a 36-year-old Douglas-fir forest in Washington State (Cole et al, 1967), and 2% of aboveground biomass of a 42-year-old Douglas-fir forest in Washington, U.S. (Turner and Long, 1975). Coarse woody debris can represent a large C storage pool in a forested ecosystem (Means et al 1992; Harmon et al, 1986). Coarse woody debris includes a wide variety of types and sizes of materials such as snags, logs, chunks of wood, large branches, and coarse roots.  22  Forest litter is also important in forest systems (Maguire, 1994; Harmon et al, 1990a; Edmonds, 1980). Forest litter represents biomass originating from the overstory, shrub layer, and herbs, all of which have variable decomposition rates. The litter occurs as a carbon pool only in the forest budgets as litter is commonly raked from the lawns in the urban area, during yard maintenance. In forest systems, roots are generally divided into three main categories: coarse, small, and fine. Grasses have only fine roots. In a forested system, the roots are distributed in no set pattern across the landscape. This is different from roots under grasses that are seeded in a systematic pattern. It is accepted that roots are important in terms of forest ecosystems (Santantonio et al, 1977; Gale and Grigal, 1987; Vogt et al, 1986; Keyes and Grier, 1981), with less attention focused on this role in agricultural systems. Total root biomass of a 36-year-old Douglas-fir stand in the Pacific Northwest was estimated at 1,650 g C m" (Cole et al, 1967). Roots were 2  estimated at approximately 3,650 g C m" on 40-year-old Douglas-fir stands in western 2  Washington, U.S.A. (Keyes and Grier, 1981). Roots represented approximately 17% of total above- and belowground biomass of 40-year-old Douglas-fir forest in western Washington (Keyes and Grier, 1981). Trees generally develop lateral roots growing parallel to the surface of the soil. They are predominantly located in the top 30 cm of soil (Gilman, 1990). Fine roots emerge from the lateral roots (Gilman, 1990). Many factors including stand age and site productivity affectfineroot biomass. For example,fineroot biomass is positively correlated with age, at least up to canopy closure (Vogt, et al, 1983). Fine root biomass on the above low productivity 40-year-old Douglas-fir site in the Pacific Northwest was three times greater than on the high productivity site (Keyes and Grier, 1981). Similar results forfineroots based on site productivity were found on second growth Douglas-fir stands on Vancouver Island, B.C. 23  (Kurz, 1989). As stand age and productivity affects fine root biomass, fine roots should be measured. In any field study there is inherent variability as a consequence of both natural heterogeneity and management practices. Urban land use is particularly complex and is characterized by great spatial heterogeneity of plant cover and age structure of vegetation, as well as species numbers, as a result of a deliberate mixture of landscape treatments which include trees, shrubs, gardens, and lawn areas occurring in no set ratios. The number of parameters of the urban ecosystem reflects this. The parameters of the urban system include trees, shrubs, annuals, lawn, tree coarse roots, tree, grass, and shrub fine roots, and soil. Typical of urban landscaping, garden areas are set aside for growing annual plants. However, very little data exists on the biomass production of annuals. The quantity of biomass produced by annuals varies as a function of the chemical and physical properties of the soils, and the site location of the plant related to lighting conditions to which they are most suited. Soil is included in the C budgets. It is not only a major C sink but as well soil C can have long residence times. For example, C in cultivated grassland soils in the 90-120-cm depth has been measured at 7,015 years before present (BP) (Paul et al, 1997). The non-hydrolizable C at this depth was dated at 9,035 years B.P. Cultivation has been found to increase the mean age of soil C, for example, by 900 years. Paul et al. (1997) reported that the age of soil C has also been found to increase with depth, and was 1,200 years older in the 10-20-cm depth, in both natural and cultivated soils, compared to the 0-10-cm depth (Paul et al, 1997). Thus, C in soil represents a long-term sink.  24  3  IMPACTS O FLAND U S EO NC A R B O N S T O R A G E A N D A S S I M I L A T I O N R A T E S IN A B B O T S F O R D , B . C .  3.1  INTRODUCTION  Two types of C budgets are required: one based on net primary productivity (NPP) and one based on the amount of C in storage. The NPP describes the amount of C assimilated by living plant material, on an annual basis - a short-term time scale. The amount of C in storage reflects the accumulation of C over time - a long-term basis. Both are important to this study. In the agricultural and urban systems, the grasses are cut on a regular basis at various frequencies that may affect the amount of C in storage. Therefore, NPP data need to be collected for these two land uses. In addition, NPP is an essential and a very important input parameter for the CENTURY model particularly as it is used to calibrate the model when producing site-specific simulations (Metherell et al, 1993). It also furthers the understanding of the C dynamics of these ecosystem types. Therefore, data on both the amount of C in storage and the NPP were collected. With the study area and parameters on which to collect data identified, site selection could be made, the data could be collected, and the C budgets could be developed. The objectives of this chapter are to describe: 1) how the study sites were selected; 2) the methods of data collection; 3) the C budgets which were prepared, and 4) the comparative analysis of the budgets.  25  3.2  METHODOLOGY  3.2.1  Site Selection and Description  Several reconnaissance visits were made to the study area to select the sites. The sites needed to be well-established, rather than recent to assess the effect of land use rather than short-term or transitional effects. For example, high rates of change in soil C usually occur immediately following a disturbance (Houghton, 1995). However, on agricultural lands in production for an extended period of time, C inputs and outputs have been found to be nearly balanced (Cole et al, 1993). In a forest, Knoepp and Swank (1997) found that soil C generally declined in the first year following whole tree harvest and became stable after 14 years. The soils information (Luttmerding, 1981) is presented on an aerial photographic basis. Preliminary sites located on Marble Hill or Abbotsford Soils were noted on the soil maps. The forested sites werefirstvisited in the fall of 1995. Thefieldinvestigation indicated that many of the forested sites identified on these soils had been converted to other uses. Forested sites which occurred on these soils and which were dominated by unmanaged second growth trees were noted. Owners were contacted and permission was obtained for afieldstudy on three of the sites. The forest sites were approximately 1.5 ha in size and located within 2 km of each other. Each site is separated by rural residences and farms (Figure 3.1a)  26  AD: Abbotsford Soils, MH: Marble Hill Soils (see Legend, Appendix B)  (Luttmerding, 1981)  F i g u r e 3.1a Forest (F) site locations/soils map, A b b o t s f o r d , B.C. (not to scale).  The agricultural areas were visited in fall 1995. Several farmers were interviewed to determine the length of ownership, past and present management practices, and future plans for their operations. Farmers applying chemicals, such as herbicides, or planning to make changes to the sites during the 1996 field season, were not included in the study. Four well-established farms were selected, separated from each other by land in raspberry production (Figure 3.1b).  27  AD:Abbotsford Soils, MH: Marble Hill Soils (see Legend, Appendix B)  (Luttmerding, 1981)  Figure 3.1b Agricultural (A) and urban (U) site locations/soils map, Abbotsford, B.C. (not to scale).  28  The hay grown on Sites Al, A3, and A4 was harvested by off-site farmers. The time of harvesting was determined by crop growth and weather conditions. Harvesting during the 1996 growing season was carried out in June and September at Site Al, in June and August at Site A2, in May, June and August at Site A3, and in May, June, July, and August at Site A4. Manure was applied in April on Site A2 and in May and July on Site A4. Thefieldswere not irrigated. The urban area was selected in the early summer of 1995. It is bounded by Sumas Way on the north and east, Glady Avenue on the west, and Old Yale Road on the south (Figure 3.1b). It fulfilled the criteria of being well established, since it has been 30 years since it was converted from a forested site to a subdivision. Site selection in the urban area was based on a stratified random system. Houses listed for sale were not selected for inclusion into the study to insure regularity in management. Houses occurring on lots built-up with fill were excluded, as these would no longer occur on native soils. Letters of introduction describing this research project were delivered by hand to potential residences. Several visits were made to the area during the site selection process, as owners frequently were not available or able to participate. The lots used in the study were selected randomly from those willing to participate. The study involved ten residential sites numbered consecutively. Photo 3.1 is a view of one of the urban lots in this residential area. A park located within the subdivision was also included in this study. The lots varied in size and in the selection of landscape treatments. They were generally 15 metres (m) to 18 m wide and varied in depth up to 56 m. The homes are generally landscaped with shrubs and lawn interspersed with native trees.  29  Photo 3.1 View of residential lot, Abbotsford, B.C.  Photo 3.2 Urban park (Larch Park), Abbotsford, B.C. 30  Management varied among the sites. Rhododendrons and ornamental trees are periodically clipped. Some of the mature trees have also been topped. In the summer, mowing is generally carried out weekly, and is more frequent during periods of heavy growth (June). Lawn treatments ranged from intensive to low, with some owners applying moss killers, herbicides, fertilizers, and dethatching. Where lots were heavily shaded, the grass was frequently sparse. Watering of lawns is not a common practice in this area. Larch Park is located within the general residential area. It is approximately 1 ha in area. It is a traditional urban park containing portions left under forest cover, areas allocated to lawn, forest-grass complexes, impermeable surfaces (e.g. tennis court), and non-vegetated surfaces (dirt paths, playground) (Photo 3.2). Within the urban area, there are highly traveled roads as well as side streets, and a school with associated playingfieldsand grounds. In a study in which comparisons among ecosystems are being made, replication is a consideration. The limited number of replicates per treatment in the agricultural and forest components is a function of the rather limited size of the study area and the fact that a wide range of uses occurred. Hurlbert (1984) noted that the most common type of "controlled" experiment infieldecology involves a single "replicate" per treatment. He noted that this is "neither surprising nor bad". He explains that replication is often impossible or undesirable when very large seale systems are studied. He further notes that when gross effects of a treatment are anticipated or when the cost of replication is very great, experiments involving non-replicated treatments may also be the only or best option. Although, this study is similar to the type discussed by Hurlbert (1984), replicates were obtained: three forested, four hay, and ten urban sites.  31  3.2.2  Carbon Analysis and Reporting  Carbon is frequently expressed as biomass and organic matter in the literature. Data were converted to a C basis. For some components such as the aboveground portion of trees, dry biomass was generally accepted as being 50% C (Harmon et al, 1990b; Sollins et al, 1980). For many of the other organic materials collected in the field study, C content was established by C analysis using the Leco (dry combustion) method (Appendix B). The conversion factor for each material type is presented in Appendix B, Table B.l. 3.2.3  Data Collection  3.2.3.1 Forested Site  On the first visit to the forested sites, two plots, 8 m x 8 m, were randomly selected on each site and stratified to include Douglas-fir trees. The forest sites were of limited area, which restricted plots size. The plots were marked with large steel pegs at each corner with the overstory flagged with tape to easily identify the corner peg locations on return visits. At each return, the plot markers were located and the plots re-enclosed with flagging tape to ensure the inspections were carried out within the plot boundaries. The litter and soils were sampled during the winter 1995. A vegetation inventory was carried out during the summer 1996 to relate the site information to the studies reported in the literature. A third visit was made to the sites in the fall of 1996 for a second litter sampling. 3.2.3.1.1  Trees  The forests were unmanaged so the trees at the sites ranged in age from saplings to mature trees, up to 70 years in age. A review of the literature indicates that the biomass of trees can be estimated using allometric equations (Gower et al, 1987; Gower and Grier, 1989; Keyes  32  and Grier, 1981) using a measurement such as the diameter at breast height (DBH) (1.4m) and tree height. The height and diameter of the larger trees were measured. Cores were obtained from four of the larger trees to determine tree age. The stems per hectare were based on the average number of trees located on the plots on a per site basis. 3.2.3.1.2 Understory Plots were flagged and divided in half to facilitate taking an understory inventory. A numbered clothes peg was clipped onto each shrub in sequence. Each shrub was identified and the percent cover of the subplot by each type was visually estimated. 3.2.3.1.3 Coarse Woody Debris The amount of coarse woody debris (CWD) varied across the sites. Information on CWD was obtained from the relevant literature on Douglas-fir stands in the Pacific Northwest to estimate the C in storage for this parameter. 3.2.3.1.4 Litter There appears to be no standard sampling period for forest litter. Summer and fall seem to be the more common periods for collection, probably the result of the logistics of access into thefield.Prescott et al. (1995) collected forest litter from Vancouver Island, B.C. sites, in the summer, the fall, and winter. Grier and McColl (1971) collected forest litter in July, under Douglas-fir stands in western Washington. Edmonds (1980) collected forest litter samples in the fall, in coniferous forests in western Washington. Fall is considered the peak period of needle and leaf litter drop in the Pacific Northwest (Gessel and Turner, 1974; 1976). Gessel and Turner (1974; 1976) collected litter on a monthly basis. Based on these studies, litter samples were collected from each plot in the late winter-early spring and at the end of the  33  growing season prior to leaf drop, in order to measure the peak and minimum amounts. The litter collected in the late winter-early spring period represented the litter deposited on the surface after leaf drop and soft tissue die-back but before decomposition had occurred. Litter collected at the end of the growing season prior to leaf drop and soft tissue die-back of nonwoody perennials represents the lowest amount of litter during the year. As microbial decomposition occurs during the growing season, the difference between the spring and fall sampling periods represents the amount lost to decomposition through the current growing season. Therefore, the maximum and minimum amount of litter was measured by collecting during these sampling periods. The litter sampling sites were randomly selected within the plots. Grier and McColl (1971) found that forest litter analysis requires several samples within a plot due to within-plot variability related to differences in the amount and type of understory vegetation. Between seven and eight samples were collected in the late winter-early spring of 1996 from each site. Three tofivesamples were collected per site in the early fall of 1996. The samples from each forest site represent one replicate, yielding three replicates in total, one from each experimental unit. As the amount of C on a square metre basis was required, the physical area from which the sample was collected must be controlled. Afiberglassrectangular frame, 13-cm x 18-cm, equipped with a sharp metal edge was placed on the ground surface (Photo 3.3). The litter was cut along the outside edge of the frame with a keyhole saw. It was then removed from the frame area. Extra care was taken at the litter-soil interface to avoid incorporating mineral soil with the litter. The samples were oven-dried at 70° C for 24 hours, according to Prescott et al. (1995) and Gessel and Turner (1976). The litter samples were ground in the Wiley Mill. Carbon content was measured at 41% of the biomass. Subsamples 34  were dried to 105° C to provide C measurements on an oven-dried basis. The C:N ratio of the litter was measured. Nitrogen was measured using the Parkinson and Allen Digestion for Foliage method (Appendix B). 3.2.3.2 Agriculture (Hay)  Subplots were set up at each farm to obtain a measure of variability of biomass production and soil organic matter within the experimental units. The subplots were selected in a stratified random manner to avoid edge effects or areas of disturbance. Three subplots measuring 1-m x 1-m were located on three of the four farms. Permission was granted for two on the fourth farm. The hay fields were first sampled in the early spring of 1996. These first cuttings represented the hay that had grown following the last harvesting of the previous year. Therefore, these samples were not included in the 1996-growing season data. Hay was collected from the farms throughout the growing season, prior to harvesting. Permanent plots could not be set up as the mature hay crop concealed markers that had been set at ground level to reduce obstruction to harvesting equipment. Therefore, sampling was carried out just prior to harvesting in the same vicinity in the fields each time. Samples were also taken in the spring of 1997 to collect the hay that had grown following the last harvest of the 1996-growing season. The biomass collected in 1996 and in the spring of 1997 was used to estimate NPP for the 1996-growing season.  35  Photo 3.3 Forest litter sampling.  Photo 3.4 Typical aboveground: belowground ratio of geranium plants from Abbotsford, B.C.  3.2.3.2.1 Hay  Sampling was carried out using a 1-m x 1-m reinforced wooden frame. This was used throughout the study for vegetation sampling to reduce systematic error by insuring that the sampling was carried out on a metre square area. The vegetation was clipped using a portable set of battery operated clippers that were equipped with extensors on the base set at 2.5 cm height to simulate harvesting equipment height. This insured consistent length over the plot area and throughout the growing season. Harvested samples were weighed wet on-site and sub-samples were taken for moisture content and C analysis. These were weighed wet and oven-dried to 105° C to obtain a moisture content. The NPP could then be reported on an ovendried basis. The data from the subplots were averaged to produce a sample mean for each farm. Carbon content of the hay was measured at 42%. Biomass produced aboveground can be divided into two components. The material continuously clipped represents the NPP. The standing grass left as stubble represents C in storage. In the early spring of 1997, two surface sample cores were collected for stubble measurement from Sites Al, A2, and A3. Three were collected from A4. The cores were taken using a 6-cm diameter bulk density sampler placed approximately 4 cm into the soil, such that the surface stubble was not disturbed. The cores were then taken to the laboratory for separation into live stubble, moss, and dead material. The vegetation was cut using a sharp razor blade and oven-dried to 105° C. The biomass was separated into the components using tweezers and weighed. This data were used in the C budget of the hay system, were later compared to lawn, and used to calibrate the CENTURY model.  37  3.2.3.3 Urban Sites The challenge of this research was to connect the urban system to the agricultural or forested systems so they could be compared. As noted previously, as a way of approaching this, the separate components that make up an urban system were viewed as potential corresponding pools to those in the other two systems. For example, lawn was considered as a proxy for hay; and trees, shrubs, and annuals as being representative of the overstory and understory in a forested system. A portion of the lots was mapped in detail, according to the following categories: lawns, gardens, trees, and impermeable surfaces. This was required as gardens, trees, and impermeable surfaces frequently occurred intermixed with the lawn areas. Calculation of the lawn area was required to relate productivity on an area basis. 3.2.3.3.1 Trees  The trees on the residential sites were identified. The DBH and height were measured on five of these trees to relate them to those on the forested sites. Cores for aging were not an option in the urban area. 3.2.3.3.2 Lawn Lawns produce biomass and the clippings represent the net primary productivity of lawn. The clippings can be likened to the hay clippings, except that the lawns are mowed more frequently than the fields are cut for hay. However,- as both types are grass-based, net primary productivity of the two can be compared. Grass clippings were collected during the growing season of 1996. Homeowners were presented with three different ways of participating in the grass clipping component of the study. Some owners mowed their lawns and weighed the clippings. 38  They recorded the weight on forms provided to them. The forms also included columns for information on fertilizing, liming, etc. Identification tags and sample bags were supplied. Owners sub-sampled the clippings which were picked up during the week. Others bagged the total amount of clippings. These were weighed on-site. Sub-samples were then collected and brought back to the lab for analysis. The third method involved the permanent establishment of one metre square plots located at the edge of the lawns, in less visible locations. These plots were established by placing the 1-m wooden frame on the lawn. The corners were marked with steel pegs. The frame was then placed over the pegs each time the plots were cut using the same clippers as on the hay plots. When not being clipped, flagging tape was tied to the pegs to remind the owners not to disturb them. They were clipped on a regular basis depending on grass growth, to 2.5 cm to compare them to the hay study. Some homeowners wished to participate in terms of the front- or backyard. Others provided the information and sub-samples for both the front- and backyards. Two 1-m plots were located in Larch Park. One was located in the open grass area. 2  The other plot was located in a forest-grass complex, to relate to the portion of the park mapped as such. The lawn portions of the park were mowed every two weeks by the city. The plots were sampled just before they mowed. The grass clippings were dried at 105° C. Carbon was measured at 42% of biomass. The grass clippings collected during the growing season are removed from the system and therefore not converted to C in storage. The C in storage, in the grass system, is the standing amount of biomass left on the surface after cutting. This remains during the winter months. Falk (1980) notes that the C budget of lawns requires the inclusion of stubble. The C in storage also represents the litter left on the surface and other plants such as mosses. Nine cores were collected from six of the 11 sites in the spring of 1997 in order to measure the 39  amount of biomass left from the 1996-growing season. Cores were taken using a 6-cm diameter bulk density sampler following the same methodology as for the hay crop, and placed approximately 4 cm into the soil. Similar to the hay data, the data were used in the C budget of the lawn system, were compared to that of the hay, and were also used as a check against the simulations of the CENTURY model.  3.2.3.3.3 S h r u b s ( R h o d o d e n d r o n s )  The preliminary visit to the urban area indicated that rhododendron shrubs were commonly used as a landscape material in the study area. Thus, rhododendrons were used to represent the shrub component of the urban system. The methodology developed for them should be suitable for sampling other types of shrubs grown in the urban area. To obtain an estimate of the net primary productivity and the C in storage, ten rhododendrons were selected for study. They were numbered in sequence of measuring, e.g. Rl was the first shrub sampled. Rhododendrons vary in their dimensions based on such factors as crowding, pruning, and the amount of shade. The height, maximum crown diameter, stem basal diameter, and age were measured in the spring of 1996 (Table 3.1). The age of each shrub was calculated by counting the wood rings and bud-scale scars of both the stem and branches, as described by Whittaker (1962). The rhododendrons measured varied in age, from 8- to 30-years-old. In order to achieve the circular shape from the ground up, rhododendrons branch just above ground level at an early age, such that the measurement of the main stem can only be carried out near ground level. The basal diameter of shrub Rl was estimated based on the relative  40  measurements of the other shrubs as this shrub branched at ground surface such and no main stem could be measured. Table 3.1 Dimensions of rhododendrons. Shrub  Age Height Crown Basal Growing (years) (cm) DiameterDiameter Tips m' (cm) (cm)  2  R3  8  150  150  9  53  R2  9  115  140  8.5  52  R4  10  210  210  9.5  65  Rll  11  170  160  10.5  70  RIO  13  130  180  15.3  102  R9  15  155  160  17  224  R6  20  250  150  19  212  R7  20  170  160  15  198  R8  20  180  230  19  232  Rl  30  300  330  -30  230  Some research has been carried out on rhododendrons, primarily in a forested setting. Means et al. (1994) and Stanek and State (1978) provide regression equations for estimating above-ground rhododendron biomass based on basal diameter at ground level. The equations presented by Stanek and State (1978) were developed by Telfer (1969). The equations provided by Means et al. (1994) were also possibly based on Telfer's (1969) findings, as they referenced Stanek and State (1978). Telfer (1969) developed these equations through destructive sampling of the aboveground portions of rhododendrons in forested sites. However, none of the equations developed could be used for this study as they are based on diameters well below the range of those found in the study area. Yarie (1978) also estimated aboveground biomass and NPP of rhododendron shrubs in forested sites. His measurements included basal diameter at ground level and clipping to ground level. He used these to develop regression equations that could be used to estimate biomass based on the basal diameter. However, Yarie (1978) did not provide data on the physical characteristics of the shrubs, height, basal diameter, etc. This presents doubt on the suitability of these equations for this study as they may not 41  have been developed for shrubs with the large basal diameters occurring in the study area. The rhododendrons in an urban area are likely larger than those in forested areas as, in forested areas, they occur as understory. In an urban area, they frequently stand alone and receive full light allowing maximum growth. The basal diameters increased by approximately 1 cm every year such that the basal diameter of the 20-year-old shrubs was 20 cm (Table 3.1). Whittaker (1962) estimated the aboveground NPP and biomass accumulation of rhododendrons {Rhododendron maximus) ranging from 11 - to 40-years-old. He used destructive sampling methods. Growth in a rhododendron occurs not only in the new tips but as well in the branches and stems of the shrubs, which increase in diameter with time. Whittaker (1962) allocated NPP and biomass percentages to the wood, branches, and old leaves as well as to the current stems, leaves and fruit, for each age of shrub. As only clippings of the rhododendrons were permitted for this study since the shrubs were located in the yards surrounding family homes, the NPP and biomass accumulation of the aboveground portion of the rhododendrons were estimated using the same relationships provided by Whittaker (1962). That is, both NPP and biomass are based on the biomass of the stems, leaves and buds collected in 1996. As the only means of relating to Whittaker's (1962) data was through the current year's biomass of stems, leaves and fruit, the total number of growing tips was calculated for each shrub. Flagging tape was tied around the stems located at each corner of a metre square area on the outer surface of each shrub. When the shrubs were small, the shoot-counting square was made smaller and scaled up to a 1-m basis. Permanently numbered clothes pins were 2  sequentially clipped onto the stems to facilitate counting, giving an overall number of the current year's growing tips per square metre.  42  To convert the number of growing tips from a metre square basis to a shrub basis, the surface area of the shrub was required. Rhododendrons are characterized by large leaves and woody stocks with the outside surface fully stocked with leaves. These are supported by a system of branches, which have split to produce a network of branches to support a circular shrub. This is different from a Douglas-fir tree that has a main stem and a system of layered branches with spaces between branches with small needles. To calculate the surface area, it was assumed that the crown area of the shrubs has the shape of a sphere, and the formula of a sphere, 4n r , was used to estimate the surface area of the shrubs. As the rhododendrons were 2  not uniformly spherical, the maximum diameter and height of the crown of each shrub were averaged to obtain a radius for insertion into the formula. As rhododendrons mature, the base of the shrub becomes shaded, resulting in the base becoming leafless. To accommodate this, the proportion of the base affected by this was estimated using the calculation for measuring the surface area of a solid angle (personal communication with Dr. M. Novak, U.B.C.). The surface area of shrubs older than ten years was reduced by approximately 4%. Based on the surface area, the total number of growing tips was calculated for each shrub. Selected stems, which included the buds/fruit and leaves, were clipped in the winter-late spring of 1996. The sample size included 101 stems, buds, and clusters of leaves. The stems, leaves, and buds were oven-dried (105°C) and weighed separately, providing a growing season biomass measure of new growth. Therefore, the net amount of C assimilated by each shrub could be calculated. The biomass for each component, stem, leaves, and buds/fruit was averaged to provide one value for each of the separate components. The C content of the leaf and fruit biomass was 46% and the stem biomass measured at 43% C. These clippings represent the growth that occurred in the 1995-growing season. The 1995 growth was used as it contributed to the physical dimensions of the shrubs and therefore to the crown diameter and 43  height. This data were used to calculate the total aboveground NPP and biomass accumulation using the relationships developed by Whittaker (1962). A review of the proportions allocated by Whittaker (1962) to the percent wood, old leaves, currents stems, leaves and fruits, both in terms of the NPP and biomass, indicated that the shrubs could be separated into two major age groups: 10 years (8 to 11 years) and 15-30 years (13 to 30 years). In Whittaker's study (1962), the ten-year category had a greater percentage of old leaves compared to the older shrubs, and a smaller wood component. This is likely related to the fact that the surface area of the younger shrubs was not fully occupied by growing tips, and therefore, the previous season's leaves would still remain on the shrubs not having been shaded out. With rhododendrons, the inner branches become woody as they age and they lose their leaves, likely due to shading. The maximum number of growing tips on the rhododendrons per metre square of crown surface reached 225. This occurred at roughly 15 years of age (Table 3.1). The number of growing tips for the 10-year-old shrubs, (8 years to 11 years), were at approximately 25% of full coverage, i.e., at "full canopy closure". Similar to trees, the proportions of biomass allocated to the woody components increased with age. Therefore, the percentages allocated by Whittaker (1962) to the various components of the shrubs were averaged for the 10-year, and 15- to 30-year-old categories (Table 3.2). Table 3.2 Biomass (%) and NPP (%) component allocation for rhododendron shrubs (after Whittaker, 1962).  years  NPP (%)  Biomass (%)  Age Wood (stem, branches)  Old leaves  Current stems, leaves, fruit  Wood (stem, branches)  Current stems, leaves, fruit  Old leaves  10  27  59  14  25  20  55  15-30  70  20  10  44  12  44  44  As the shrubs investigated ranged from 8 to 30 years, it was decided that the rhododendron data would be stratified into 5-year growth periods. That is, the data calculated would be averaged for 10-, 15-, 20- and 30-year-old rhododendrons. This would be useful in developing C budgets; that is, in calculating the amount of C stored per metre square in terms of 10-year-old shrubs versus 20-year-old shrubs. A correlation analysis found basal area and age were highly correlated (94%), age and average diameter (80%), and age and number of growing tips per metre square (86%). Thus, the age of the shrubs is correlated with height and basal diameter, and therefore, to biomass and NPP (Table 3.1). Therefore, subdividing the shrub analysis by age group was considered suitable for rhododendrons. The amount of C per metre square both in terms of C in storage and NPP, was calculated by estimating the ground area occupied by each shrub. This was based on the diameter of the crown obtained by averaging the height and diameter of the crown. The total C per shrub was then adjusted to a metre square basis. 3.2.3.3.4 A n n u a l s Plants (Geraniums)  A common annual occurring in the urban area is the geranium (Pelargonium). Thus, it was selected as the representative for the annual plant component. These plants are medium sized in relation to the wide range of annuals growing in the study area. Although geranium plants were selected as representative of the annuals in the urban area, the methodology developed to assess the amount of C assimilated per metre square should be suitable for estimating the NPP of other annual plants. Nine mature geraniums from one garden were destructively sampled at the end of the 1995 and 1996 growing seasons. The aboveground portion of the plants was separated from the below-ground portion. The soil was washed from the root ball and the aboveground part of the  45  plant and the roots were ground separately for C analysis. The biomass of the geraniums was calculated on a metre square basis. The plants were oven-dried to 70° C for 24 hours. Carbon was measured at 37% of biomass for the stem and flowers and at 40% for the roots. Samples were then dried at 105° C.  3.2.3.4 Soils The C inputs into the soil originate aboveground from the decomposition of CWD (Busse, 1994), understory, and leaf litter (Fried et al, 1990). Below-ground, C inputs originate from roots (Walton, 1983) and microbial populations (Ellert and Gregorich, 1995). Fine roots, through dieback, contribute on a continuous basis to the soil organic matter (Walton, 1983). The proportion of microbial C is estimated to be relatively small, at 2-5%> of total soil C (Ellert and Gregorich, 1995). Soil C can be expressed in different ways. It can be expressed as percent C (Elbert and Gregorich, 1995; Ranger et al, 1995) and on a volume basis (Knoepp and Swank, 1997; Paul et al, 1997). Percent C is an unsuitable measure for this study unless coupled with the bulk density (Db) due to the effects of land use on Db. The same percent of C will represent a different mass of C on a soil with a low Db compared to one with a high Db as more soil mass occurs in a set volume in soils with a high Db with the same percent C, compared with soils with a low Db. Reganold and Palmer (1995) note that presenting data only on a volume basis may cause misinterpretation of the data where differences in Db occur. Further, as the treatment of the soils is so different, sampling to a specific depth was considered the best option to accommodate this. Sampling to specific depths rather than by horizon is a method previously used in soil studies (Paul et al, 1997; Knoepp and Swank, 1997; Ranger et al, 1995; Harding and Jokela, 1994). Therefore, to measure the amount of C in the soils, Db  46  measurements and percent C were combined. This permitted the calculation of the amount of C in grams in the soil to a set depth. With the depth and diameter of the corerfixed,the data could be reported on a spatial basis for a specific depth. Bulk density samples were taken at the 0-10-cm depth and the 10-20-cm depth. The results from the two depths were amalgamated for thefinalbudgets and for modeling with CENTURY. The 0-20-cm depth is used in the CENTURY model. Sampling to a standard depth is potentially problematic for the purposes of providing a C budget. However, the depth was restricted by the model. Soil sample collection in the forested and agricultural areas was carried out at each site and within each subplot in the spring of 1996. Bulk density measurements were taken using a 10-cm diameter, 10-cm deep bulk density sampler. In the forested sites, the litter and Db cores were taken from the same area. Generally, three to four Db measurements were made at each agricultural and forested site. These were averaged on a per site basis and were treated as one replicate. The soils were sampled adjacent to the hay sampling plots. This yielded three replicates for the forested use and four for the agricultural use. In the urban area, soil samples were collected in the spring of 1996 from a portion of the lawn and garden areas on the residential sites and in Larch Park. Traditional Db sampling was not permitted in the urban setting due to the amount of disturbance involved. Therefore, a 1.5cm diameter soil sampling probe, 27cm in length, was substituted for the large corer. The 1.5cm corer was inserted under pressure from the surface, with less control, in terms of angle and sampling depth, as the 27-cm probe was longer than that required for this study. The soil cores were difficult to obtain, likely due to the thick sod created by the turfgrasses (Beard and Green, 1994; Emmons, 1984). Ten soil samples were collected from lawn at six sites in the 0-10-cm depth. Eight were collected from four sites in the 10-20-cm depth. Front yards were considered separate from 47  back yards as they are managed differently. With the smaller cores, generally four sample cores were collected per site. These were composited to obtain sufficient sample for analysis. These composited samples are considered replicates as all were collected from separate sites that are subject to different management programs. Samples were also collected the grass and forested areas of Larch Park and under two rhododendron shrubs. The soils were air dried and passed through a 2-mm sieve. Roots were removed (and weighed separately) from the samples, using tweezers, to achieve a measurement of the amount of C per gram of soil. Sub-samples of the soils were dried at 105° C to obtain the data on an oven-dried basis. To evaluate the accuracy of the small probe to estimate Db and provide a conversion factor for correction (regression equations), a study was carried out which involved comparison of the two methods. This was carried out in the hay fields. Samples by both methods were taken adjacent to each other. The agricultural soils were used for this rather than the forested sites as although they receive less traffic than the lawn areas, they are traversed several times a year during harvesting and manure spreading. As well, both are grass crops. In grass systems, roots are fine, and occur at relatively uniform spacing due to the method of seeding. In the study area, the forest soils are not traversed by equipment or exposed to high foot traffic. As well, more variability occurs in root size and distribution in forested systems. The C content of the urban soils was corrected on a site by site basis, in that, if the Db was reduced by 12%, using the regression equations, the soil C content was also reduced an equal amount for that particular site.  48  3.2.3.5 Roots  3.2.3.5.1 Coarse and Small Roots Actual measurement of coarse and small roots of trees requires destructive sampling. This has been carried out for Douglas-fir in the Pacific Northwest (Thies and Cunningham, 1996; Cole et al., 1967). Because of the difficulty of such sampling, coarse and small root biomass is commonly estimated using regression equations based on aboveground measurements (Kurz, 1989; Keyes and Grier, 1981). As data on second-growth Douglas-fir trees in the Pacific Northwest and southwestern B.C. is available, the C content of this pool was estimated following a literature review. 3.2.3.5.2 Fine Roots Fine roots for all land uses were extracted from the soil samples (to 20cm). A review of the literature indicates that there is no consistent fine and small root sampling depth. Rooting depth is affected by several factors including soil compaction and aeration (Hopkins and Patrick, 1969), soil texture (Gale and Grigal, 1987), tree species (Gale and Grigal, 1987), succession status (Gale and Grigal, 1987), soil fertility, and depth to the water table (Gilman, 1990). Gilman (1990) notes that tree roots are generally located in the top 30 cm of mineral soil. Vogt et al. (1983) noted that more than half offineroot biomass was located in the forest floor. Keyes and Grier (1981) estimatedfineroots to a depth of 45 cm. Kurz (1989) studied fine roots on Douglas-fir stands on Vancouver Island, B.C. in the forest floor as well as to 30 cm and 50 cm depths in the mineral soil. Kurz (1989) noted that 55% of the totalfineroot biomass occurred in the upper 30 cm of mineral soil with 20% being in the forest floor and 25% in the 30-50-cm depth. Data on root biomass presented by Sollins' et al. (1980) were  49  based on a depth of 100 cm. This variability in sampling depth indicates no accepted standard for depth and prevents direct comparison between the results of this study and the data reported in the literature. Fine roots were separated from the soil by hand using tweezers (Kurz, 1989; Sollins et al, 1980; Santantonio and Grace, 1987; Keyes and Grier, 1981). In their study, Keyes and Grier (1981) removed thefineroots by sieving the soil, the smallest sieve opening being 2 mm. However, because the diameter of the roots were less than 2 mm, sieving was not carried out for this study as some roots passed through the 2 mm openings. Data presented in the literature was used to estimate the amount of rhododendron root material as the rhododendron in the study area could not be destructively sampled. Root biomass was estimated using root to woody shoot ratios for Rhododendron maximus reported by Whittaker (1962). 3.3  3.3.1  RESUL TS AND DISCUSSION  Forested Land Use  3.3.1.1 Species Composition  The vegetation in the forested sites was separated into two layers, the overstory and the understory. The sites differed in terms of overstory composition, percent canopy cover and understory composition and percent cover. While Douglas-fir was a common tree species, other trees also occurred (broadleaf maple [Acer macrophyllum, Pursh], white birch [Betula papyrifera], and western red cedar [Thujaplicata, Donn]) (Table 3.3). All sites were near canopy closure.  50  The understory layer was generally a mixture of evergreen and deciduous saplings and  plants. Common vegetation included vine maple (Acer circinatum), red-berry elder (Sambucu  racemosa var. arborescens), deer fern (Struthiopteris spicant), sword fern (Polysti  munitum), bracken fern (Pteridium aquUinum pubescens), stinging nettle (Urtica lyal thimbleberry (Rubusparviflorus), red huckleberry (Vacciniumparvifolium), nine bark (Physocarpus capitatus), and tall mahonia (Berberis aquifolium). False Solomon's seal  (Smilacina amplexicaulis), trailing blackberry (Rubus ursinus), vanilla leaf (Achlys tri  western trillium (Trillum ovatum), and Oregon grape (Mahonia nervosa) were also presen  51  Table 3.3 Inventory of forest site vegetation. Site  Fl  Overstory  Understory  Plot A: 19 vine maple (Acer circinatum): 4 red-berrv elder (Sambucus racemosa var. arborescens); groundcover*: 50%; groundcover: 90% false Solomon's seal (Smilacina amplexicaulis), 10%: 2 deer Plot A : cover: 85%; 2 DF*( ht. 29 fern (Struthiopteris spicant), 1 sword fern (Polystichum m,15 m); 1 broad leaf maple (Acer macrophyllum), (ht. 30 m); 4 mature munitum), stinging nettle (Urtica lyallii), trailing blackberry (Rubus ursinus), vanilla leaf (Achlys white birch (Betulapapyrifera); triphylla), western trillium (Trillum ovatum); Plot B: cover: 95%; 3 DF ( ht. 11-19 Plot B: eastern h a l f : 33 vine maples. 15 red-berrv m); 1 mature white birch. elder; Western h a l f : 50% shrubs: shrubs: 90% red-berrv elder, 10% vine maple, 1 thimbleberry (Rubus parviflorus); groundcover*: 15%; groundcover: 90% trailing blackberry, 10% mixture of false Solomon's seal, vanilla leaf, western trillium. F l : mixture of conifers and deciduous; stems ha" : 625; 1  F2  F2: mixture of conifers and deciduous; stems ha" : 625 1  Plot A : cover: 80%: 3 DF ( D B H 12 cm; ht. 27 m, D B H 55 cm; ht. 27m; ht. D B H 55 cm); 3 mature white birch; Plot B: cover: 70%: 2 DF( ht. 25 m. D B H 37 cm; ht. 27 m, D B H 67 cm).  Plot A: eastern h a l f : 10 vine maple. 1 red huckleberry, 1 red-berry elder, 4 sword ferns; groundcover: 25-30%; groundcover*: 60% oregon grape(Mahonia nervosa), 20% trailing blackberry, 10% fem, 10% vine maple; western half": 9 vine maple. 1 red huckleberrv (Vaccinium parvifolium), 1 sword fern; groundcover: 80%; groundcover*: 50% oregon grape, 50% trailing blackberry; Plot B: 11 vine maple. 15 bracken fern. 4 red-berrv elder, 1 red huckleberry; groundcover*: 50%; groundcover*: 95% trailing blackberry.  F3  F3: 100% conifers: 50% 2nd DF, 50% western red cedar (Thuja plicata); stems ha" : 470 1  Plot A : cover: 70%; 3 DF (ht 33 m, D B H 55 cm, age 70 yr., SI 28); (ht. 32 m, D B H 42 cm, age 70 yr., SI 27); (ht. 15 m, D B H 20 cm); Plot B: cover: 70%; 3 DF (ht. 27 m, D B H 55 cm, age 60 yr, SI 25); (ht. 23 m, D B H 25 cm); western red cedar (ht. 11 m, D B H 20 cm, age 55 yr).  Plot A: understory/groundcover* 98%; understory/groundcover*: 80% false Solomon's seal, 15% bracken & sword ferns, 5% nine bark (Physocarpus capitatus), vine maple, western trillium and vanilla leaf; shrubs: 13 red-berry elder, 9 ninebark, 8 sword fem, two bracken fern, 3 vine maple; 1-m x 1m plot: 84 false Solomon's seal, 6 ninebark; Plot B: 22 vine maple. 2 nine bark. 1 tall mahonia (Berberis aquifolium), 1 sword fern, 1 bracken fern; groundcover*: 50%; groundcover*: 75% trailing blackberry, 20% oregon grape, 10% maple.  * DF=Douglas-fir trees; D B H : diameter at breast height; SI: Site Index; groundcover: low-lying vegetation; plots divided into western/eastern halves for ease of mapping.  52  3.3.1.2 Age of Douglas-Fir Stands  The trees on the forested sites ranged in age from saplings to 70 years. However, an average stand age of the stand was required to estimate the carbon in biomass and the NPP. The degree of canopy closure may be used as a means to estimating the average age. For example, Turner and Long (1975) carried out research on second growth Douglas-fir sites ranging in age from 22 to 73 years, similar to the sites under study, at the Cedar River watershed (Seattle, Washington). That part of the Cedar River watershed area is characterized by a rain-dominated winter and a relatively dry summer with a mean annual precipitation of 1440mm and mean temperature of 9.4 C, similar to the study area. The soils are developed on 0  gravelly glacial till material. Canopy cover at our sites ranged from 70 to 95%. Turner and Long (1975) found that canopy closure occurred on their sites at 42-years, when production was highest. Thus, the sites in this study, being near canopy closure, may be reflective of a 40year-old site and near to the highest production rate. Understory patterns may also reflect stand age. Turner and Long (1975) studied understory patterns on the same Douglas-fir stands. Their studies showed that understory decreased with stand age. The highest amount of understory occurred under a 22-year-old forest and the lowest amount under a 73-year-old stand. The understory under a 42-year natural stand was approximately half of that of the 22-year-old stand and it decreased to about one third in the 73-year-old stand. The nature of the understory vegetation also changed with time. Vascular plants and ferns dominated the understory in the 22-year-old stand, at 84% and 15% respectively. Mosses represented less than 1%. In the 42-year-old stand, vascular plants represented 87% of the understory. Ferns represented 6% and mosses 7%. On the contrary, mosses and vascular  53  plants dominated the understory in the 73-year-year old stand at 55% and 42% respectively, with ferns at less than 1%>. Our study sites were not 22-year-old stands with trees as old as 70 years. Nor is the understory reflective of that under a 73-year-old stand as little moss was noted. The understory on our study sites was very pervasive and the vegetation was predominately vascular plants with some ferns, similar to the conditions of the 42-year-old stand. Based on all of these considerations the representative stand age for biomass and NPP amounts has been estimated at 40-years-old. 3.3.1.3 Douglas-fir Trees - Carbon in Storage and NPP Aboveground  Carbon in storage and NPP vary with stand age and biophysical conditions (Keyes and Grier, 1981; Turner and Long, 1975). Thus, to obtain an estimate of the amount of C in storage and the NPP on our study sites based on existing information, studies of second growth Douglas-fir stands in the Pacific Northwest and southwestern B.C. were reviewed. For example, studies reviewed included Turner's and Long's (1975) study of second growth Douglas-fir sites at the Cedar River watershed (Seattle, Washington) and Cole's et al. (1967) study of a 36-year-old Douglas-fir plantation in the same site. Keyes' and Grier's (1981) study of high and low productivity 40-year-old Douglas-fir sites in the Charles Lathrop Pack Experimental Forest, southeast of Seattle, Washington was included. Their study area has an annual precipitation of 1,000 mm with over 90% of the precipitation falling between October and June. The frost-free period ranges from 130 and 180 days and the mean January and July temperatures are 2.4 and 18.1°C, respectively. The climate is similar to our study area. On Vancouver Island, B.C., Kurz (1989) sampled Douglas-fir stands of varying productivity. The stands were located in the very dry, maritime subzone of the Coastal Western Hemlock  54  biogeoclimatic zone. These stands included trees between 32 to 70 years of age, similar to the range in this study area. The review indicated a wide range of values for these parameters. For example, aboveground C for a 42-year-old natural Douglas-fir forest in Washington, was estimated at approximately 10,250 g m" (Turner and Long, 1975). The soils were developed on a very coarse textured, gravelly, glacial till deposit. Turner and Long (1975) compared the biomass of different aged trees and found that total biomass increased from approximately 6,600 g C m"  2  on a 22-year-old site, to 10,000 g C m" for a 42-year-old site, to 15,000 g C m" on a 73-year2  2  old site. The aboveground C content of the 36-year-old Douglas-fir trees studied by Cole et al. (1967) was estimated at approximately 8,600 g m" . The soils are developed on glaciofluvial material. The lower biomass of the 36-year-old trees is presumed to be primarily a function of age. As a relatively young stand, the trees have not yet accumulated extensive biomass. In the study by Keyes and Grier (1981) on 40-year-old stands, the sites were differentiated on the basis of soil texture. The soils on the low productivity site were gravelly loamy sand developed on glacial outwash with a coarse fragment content of greater that 66%, by volume. The soils on the high productivity site were silt loam in texture developed on a colluvial soil mixed with lake sediments with less than 10% coarse fragments. The soils in our study were developed on a non-gravelly, silt loam, aeolian capping overlying, very coarse textured glaciofluvial material. The capping can be as shallow as 20 cm and as deep as 85 cm. It is assumed that because of the underlying gravelly soils, the trees in some portions of our study area will be subject to soil moisture stress during the growing season where the capping is shallow. Thus, the study area site may represent an average of the sites investigated by Keyes and Grier (1981) at 17,900 g C nf , the averaged between 23,400 g C m" for the high 2  2  productivity site and at 12,430 g C m" for the low productivity site. 55  To check if the biomass could be averaged between the high and low sites, a test using a forest yield computer program, Tipsy Version 2. Id, was carried out. For this test, stand yield was run for coastal Douglas-fir at site indices of 24 (the low productivity site), 40 (the high productivity site), and 32, an average (Appendix B, Figure B.l). Based on this test, it appears that averaging is a reasonable approach. Understory may also reflect site productivity. Sixteen second growth high and low productivity Douglas-fir stands were studied in the Pacific Northwest (Vogt et al, 1983). The high productivity stands have a characteristic understory compared with the low productivity stands. The major understory vegetation was bracken fern and vine maple on the high productivity sites and salal and oregon grape on the low productivity stands. On our sites, bracken fern and vine maple were well represented (Table 3.3) with some Oregon grape. Thus, the dominant understory of our site appears to more closely resemble their high productivity stand. Productivity is also indicated by site index. The site index of the low productivity site investigated by Keyes and Grier (1981) was 24m and the site index of the high productivity site was 40m. At Site F3, a 70-year-old Douglas-fir tree had a height of 33 m and a 60-year-old Douglas-fir tree had a height of 27 m. Although older than 50 years, neither reached 40 m. The site index of the older trees is 28 (based on the site index curves for coastal Douglas-fir), that is, between the high and low sites. The site index ranged from 20 to 40 on the stands studied by Kurz (1989), which was similar to the low productivity and high productivity stands studied by Keyes and Grier (1981). The soils on the sites investigated by Kurz (1989) have developed on a gravelly (30% to 65% coarse fragments) sandy loam morainal blanket deposit. Kurz (1989) estimated aboveground biomass between 6,750 g C m~ to 28,650 g C m" for the range of trees. This is an average of 2  2  56  17,700 g C m", similar to the average found by Keyes and Grier (1981) at 17,900 g C m". Our 2  2  study area has been rated as Class 1 for forest land capability (CLI, 1967). As Class 1 it has no important limitations to the growth of commercial forest. Based on the above discussion and the Class 1 rating, it would seem that the C stored aboveground may be in the range of an average of the sites studied by Keyes and Grier (1981) and Kurz (1989). For this study, based on the above review, a value of 18,000 g C m" was estimated as the amount of C in storage. 2  Several researchers have measured NPP (Turner and Long, 1975; Kurz, 1989; Keyes and Grier, 1981). For a 42- year-old natural stand, Turner and Long (1975) estimated aboveground NPP at 465 g C m" yr". Kurz (1989) estimated NPP between 235 g C m" yr" to 2  1  2  1  800 g C m" yr", averaging 520 g C m" yr", for stands 32- to 70-years-old. Keyes and Grier 2  1  2  1  (1981) estimated average aboveground NPP of 525 g C m" yr' for a 40-year-old stand. Based 2  1  on these reports, the NPP for the trees in this study was estimated at 525 g Cm" yr". 2  1  These values of C in storage and the NPP were also applied to the trees in the urban area.  3.3.1.4 Understory Carbon in understory is stored in the foliage, stems and roots and generally contributes less than 5% of the total C budget in second growth Douglas-fir stands (Gower and Grier, 1989; Turner and Long, 1975; Cole et al, 1967), estimates for this component were derived from the relevant studies. The understory inventory of our sites was compared to that reported for second growth Douglas-fir forests in the Pacific Northwest. The percent of total aboveground biomass as understory decreases with increasing stand age because tree stem biomass increasingly dominates aboveground biomass (Long and Turner,  57  1975; Turner and Long, 1975). For example, biomass of the understory decreased from 5% of the aboveground biomass in a 22-year-old Douglas-fir forest (305 g C m"), to 2% (170 g C m") in a 42-year-old natural stand, to less than 1% (110 g C m") in a 73-year-old 2  2  stand (Turner and Long, 1975). Gower and Grier (1989) estimated understory at 5.6 g C m" under a 70-year-old mixed coniferous stand in central Washington, U.S.A., which included Douglas-fir. The composition of the understory also changes with stand age. For example, in a 22year-old Douglas-fir stand in Western Washington, vascular plants and ferns dominated the understory with mosses representing only 1% (Turner and Long, 1975). With increasing stand age, the vascular plants and ferns decreased and mosses increased. In a 73-year-old Douglas-fir stand, the biomass of vascular plants had decreased by 80% and ferns by 99% in comparison to the 22-year-old stand. There was a 50-fold increase in moss biomass in the 73-year-old stand, representing 55% of the understory. The understory composition of a 36-year-old Douglas-fir stand in Washington is similar to that found in the study area as it included Oregon grape, bracken fern, huckleberry, and twin-flower (Linnaea borealis) (Cole et al, 1967). No mosses were reported. Understory was estimated for that study at 40 g C m". Turner and Long (1975) 2  only sampled salal (Gaultheria shallon) for the shrub component, which is not widely represented on our sites. In their study of the 42-year-old Douglas-fir stand, vascular plants were estimated at 147 g C m", ferns at 10 g C m", and mosses at 3 g C m". Based on the 2  2  2  inventory of our sites and the studies discussed above, the understory has been estimated at 110 g C m" for the shrub and vascular components and 5 g C m" for the mosses, for a total of 115 g C m". This value is more than the estimate proposed by Cole et al. (1967) and less than 2  that of Turner and Long (1975) for stands in the range of 40-years-old in the Pacific Northwest. Vascular plants may be considered to contribute to long-term C storage as they can be 58  considered to be present as a relative constant over time, thereby forming part of a continuum. In this perspective, they can be considered as a long-term C pool. Gradually they can be replaced in response to changes in canopy closure. Turner and Long (1975) and Gower and Grier (1989) also measured the NPP of understory. Turner and Long (1975) used clippings of the current year's leaves and stems to calculate the NPP of shrubs. For the annuals and geophytes, Turner and Long (1975) assumed aboveground NPP of the annuals and geophytes was equal to their total aboveground biomass. In the 22-year-old stand, the aboveground NPP of the understory was measured at 80 g C m" yr", with annuals representing 60% of total NPP. Understory production decreased 2  1  with age, decreasing by 70% by 42 years from 20 g C m" yr' , to 5 g C m" yr" in a 73-year-old 2  1  2  1  stand. Gower and Grier (1989) measured the NPP under a 70-year-old forest at approximately 13 g C m"yr"'. Turner and Long (1975) noted that understory represents approximately 17% 2  of total aboveground NPP for a 22-year-old natural forest decreasing to 6% at 42 years. The production of understory biomass decreased by 70%> between the 22-year-old stand and 42-year-old stand (Turner and Long, 1975). Understory biomass remained relatively constant on stands older than 42-years of age, decreasing only by an additional 8%> by 73 years. In the 22-year-old stand, annuals produced 60% of the NPP of understory and salal, 40%>. At 42 years, the NPP of annuals was approximately equal to that of salal. Salal was used as an index of shrub cover. Mosses dominated understory production in the 73-year-old stand, representing 82% of total understory NPP while annuals represented 4%. Therefore, between 42 and 73 years, the understory changes substantially in composition but not in total production. The forested sites in the study area differ from the 73-year-old stand in that mosses do not dominate the composition of the understory. The NPP value used for this study was estimated at 25 g C m" yr", based on the study by Turner and Long (1975) on the 42-year-old 2  1  59  natural stand. At this age, the composition and proportions of the understory in the study area were similar (a mixture of annuals and shrubs). 3.3.1.5 Coarse Woody Debris  The amount of CWD on a site is affected by several factors. For example, the decomposition rates of coarse woody debris affects the amount on-site. The decomposition rate is affected by site conditions such as temperature and moisture (Harmon et al, 1986; Spies et al, 1988), the size of fragments, and nature of the material (Means et al, 1992; Harmon et al, 1986). Spies et al. (1988) found that in old-growth (>200-years-old) Douglas-fir forests in the Pacific Northwest, CWD biomass on dry sites was 48% less than on moderate sites, and 60% less than on moist sites. The amount of CWD is also affected by the stems per hectare, the history of the stand, stand composition, stand management, and the age of the stand (Spies et al, 1988; Harmon et al, 1986). For example, less CWD occurs in deciduous forests compared to coniferous forests due to greater rates of decay in deciduous forests related to the smaller sized material and a higher substrate quality (Harmon et al, 1986). The age of the stand is a factor for two major reasons. As trees age they decay and being older, the substrate is larger. Spies et al. (1988) reported the greatest biomass and volume of CWD occurs for older stands of 450-year-old Douglas-fir trees, with an intermediate amount in the young growth (<80-years-old), with the lowest amount occurring in mature age classes (80-120-years-old). Harmon et al. (1990b) compared the CWD in a 60-year-old-stand to a 450-year-old stand in the Pacific Northwest. They estimated 380 g C m" in the 60-yearold Douglas-fir forest and approximately 9,700 g C m" in the old growth forest. Others have 2  60  reported similar amounts in old growth (Means et al, 1992). Cole et al (1967) carried out a C budget at a 36-year-old western Washington Douglas-fir site and estimated CWD at 400 g C m". Spies et al (1988) measured CWD for a 60- to 80-year-old Douglas-fir stand in 2  the Pacific Northwest at 1,440 g C m". Turner and Long (1975) found the lowest amount of 2  CWD in 22-year-old stands at 25 g C m" . They found approximately 65 g C m" of CWD on a 30 year-old-stand, none on the 42-year-old Douglas-fir stand, and approximately 2,500 g C m"  2  on the 73-year-old natural Douglas-fir stand. Stand age and site conditions are the key factors affecting the amount of CWD. As the stands in the study area spanned a wide age range, the amount of C in the CWD pool will not be reflective of stands of 22-years-old or of 73-years-old. The CWD for our study area was estimated at approximately 615 g C m". This is more than that reported by Cole et al (1967) 2  at 400 g C m" for a 36-year-old western Washington Douglas-fir site. It is also greater than 2  that estimated by Harmon et al. (1990b) at 380 g C m" in the 60-year-old Douglas-fir forest. It 2  is more than that reported by Turner and Long (1975) as they did notfindany on a 42-year-old Douglas-fir stand, and less than that reported by Spies et al. (1988) for a 60- to 80-year-old Douglas-fir stand in the Pacific Northwest at 1,440 g C m". 2  3.3.1.6 Litter For the three forested sites, C stored in the litter layer after leaf drop, ranged from 1,027 g C m" to 1,194 g C m" (Table 3.4), averaging 1,128 g C m". A late summer litter collection, 2  2  2  prior to leaf drop, indicated a substantial reduction in C in storage at Sites FI and F2 (51% and 40%), respectively) and only a 9% reduction at Site F3. This does not seem to be related to the percent C or the C:N ratio of the litter (Table 3.4). Carbon analysis indicated less than 1% difference in percent C for the litter from the three sites, and C:N ratio differences of less than  61  five with the lowest C:N ratio found in the litter collected from Site F3. The decrease in litter at Sites Fl and F2 in the late summer is attributed to the nature of the litter. Douglas-fir trees and mature deciduous trees (maple and birch) occupied both sites. Because of the widely spread branching patterns of these deciduous trees, the canopy was dominated by them, contributing large amounts of leaf litter to the sites. The tree cover at Site F3 was coniferous, dominated by Douglas-fir and cedar. In decomposition studies, Prescott et al. (1996) found that deciduous litter (vine maple), decomposed at a much faster rate than litter from Douglas-fir and western red cedar. On deciduous sites, 75% of the litter decomposed in one year and compared to a 35% loss for Douglas-fir litter. The lowest rate of decomposition occurred under western red cedar at 20% in one year. Harmon et al. (1990b) reported similar trends. Therefore, the difference between the litter quantities collected between the late summer and winter sampling periods at Sites Fl and F2 is attributed mainly to the decomposition of the deciduous component of the litter. Based on thesefindings,it appears that coniferous litter can be sampled throughout the year with a maximum amount of variability of less than 10%, or less than 95 g C m". 2  62  Table 3.4 Carbon in storage in forest litter. Sites  Season  Sampling Population  Carbon Storage gCm(SD)  Canopy Cover  7  1,025 (393)  90% deciduous  3  500(189)  90%o deciduous  8  765 1,165 (303)  95% deciduous  5  695 (164)  95% deciduous  2  FI FI  winterspring late summer  FI  average  F2  winterspring late summer  F2  F2  average  F3  winterspring  7  930 1,195 (525)  100% conifer  F3  late summer  5  1,090 (387)  100% conifer  F3 FI, F2, F3  average average  1,140 945 (189)  Percent Loss (winterSummer)  % Carbon (SD)  C:N  51%  41 (3)  24  40%  41(3)  21  9%  42(5)  20  41  22  A review of the literature indicates variation in litter quantity in Douglas-fir dominated forests in the Pacific Northwest. Cole et al. (1967) measured litter at 615 g C m" on a 35-year2  old Douglas-fir forest in the Pacific Northwest. Grier and McColl (1971) reported approximately 585 g C m" on a 40-year-old Douglas-fir stand in the Pacific Northwest and Cropper and Ewel (1984) used a C value of 1,640 g C m" for their simulations of a 100-year2  old Douglas-fir stand in the Pacific Northwest. Despite the fact that Sites FI and F2 were dominated by deciduous litter, the quantity of litter for all three sites, for the winter sampling, was similar, averaging 1,130 g C m" . The variation in the amount of litter reported indicates that litter sampling should be carried out when estimating a C budget for a site. As the quantity of litter which occurred at Sites FI and F2 was dependent on the time of year, an average between the maximum and minimum, at 945 g C m", has been used in the C budget (Table 2  3.4).  63  3.3.2  Agricultural Land Use  3.3.2.1 Hay  The aboveground C assimilated (NPP) in the hay systems ranged from 330 g C m" yr" at Site A3 for the 1996 growing season, to 545 g C m" yr" at Site A4, the most 2  1  2  1  intensively managed of the agricultural sites (Table 3.5). NPP averaged approximately 395 g C m" yr" for all sites (Table 3.5). 2  1  Table 3.5 Aboveground NPP of hay. Site  Management NPP (gCm yf') 2  Al A2 A3 A4 Average  330 370 330 545 395  Harvest: June, Sept. Harvest: June, Aug.; Manure: April Harvest: May, June, Aug. Harvest: May, June, July Aug.; Manure: May, July  Production at Site A4 was much higher than at the other sites. At Site A4, the farmer harvested hay for silage in the spring of 1996. Therefore, the estimate for that month was based on the farmer's records, adjusted for moisture. This value may not be accurate. If this value was overestimated by 50%, the total would still be higher, at 440 g C m" yr", than the other 2  1  sites, indicative of the impact management can have on increasing production and thus C assimilation. Manure is generally the only type of amendment applied to these fields. Manure, being C based, is a particularly good fertilizer in terms of maximizing C in storage. The impact of the manure applications is indicated by the difference in crop production between Sites A2 and A4, with Site A2 having received one application in the 1996 growing season, and A4, two applications. The net result is that the NPP of Site A4 was approximately 45%) greater than at Site A2. The increase in production totally as a result of the use of manure 64  cannot be confirmed as this was not a controlled experiment. No manure was applied on Sites Al and A3, the sites with the lowest production. From the above, it follows that grasses are responsive to fertilization with manure application. With higher nutrients, e.g. nitrogen, carbohydrate reserves are greater (Walton, 1983) promoting leaf growth. There will be an increase in photosynthetic surfaces and thus C assimilation with an increase in leaf growth. Carbon assimilation can also be increased through increasing the height of cutting of the tiller (Radcliffe and Baars, 1987; Walton, 1983). During harvesting, the apical dome of forage grasses is cut as it is located above the cutting blades (Walton, 1983). Walton (1983) noted that a loss in production occurs when grasses are cut close to the ground surface due to the removal of the photosynthetically active parts of the tiller. As a result, the potential number of cuttings throughout the growing season is reduced. Grass growth follows a sigmoidal pattern and the early slow response to growth is a function of the low leaf area following harvesting, which limits the photosynthetic capacity and therefore the production of carbohydrates. Harvesting results in the removal of carbohydrate reserve material stored in the stem base, which once decreased, reduces the initial re-growth and therefore, the time before reaching the top of the sigmoidal curve. Walton (1983) reported that forage crops cut to 5 cm in height result in greater production than if cut to 2 cm. However, the typical harvesting height in the study area is at approximately 2.5 cm. Therefore, it was necessary to sample at this height. A major portion of the C assimilated aboveground by a hay crop during the growing season is not transformed into stored C. Most of the simulated C is removed from the system through harvesting. Hay does, however, provide short-term assimilation of C during the growing season. If hay is not harvested the production rate is lowered as grass crops reach a maximum amount of growth (Walton, 1983). That is, less C would be assimilated during the 65  growing season if the hay is not harvested. In the fall, grasses enter a dormant period which is affected by light and temperature (Walton, 1983). During this period, there is a sharp reduction in the growth rate (Walton, 1983). The stubble remaining over the winter is considered to represent the aboveground C in storage. The stubble measured at all sites varied widely (Table 3.6).. Carbon in storage in the stubble ranged from approximately 20 g m" to 153 g C m", averaging of 99 g C m". The 2  2  2  greatest amount of stubble occurred at Site A l , the site of lowest production (Table 3.5). The least amount of stubble occurred at Site A4, the most intensively managed site and the site with the highest production (Table 3.5). Large bare patches of soil did occur between the plants at Site A4, likely the result of higher management resulting in less weed infestation between the plants. Similarly, less stubble occurred at Site A2 that was also more intensively managed than Sites Al and A3. This also suggests that the increase in aboveground C in storage could be related to the presence of weedy species growing between the hay plants. Table 3.6 Aboveground carbon in storage in hay. Site  Sample Pop.  Stubble  Moss  gCm % of total (range) sample 2  Total Dead Grass  gCm % of total gCm (range) sample 2  2  gCm (range) 2  Al  2  152.6 (89.4-215.7)  98  3.3 (1.0-5.6)  2  155.9  17.0  A2  2  91.7 (90.7-92.7)  50  91.7 (90.7-92.7)  50  183.4  0.0  A3  2  131.9 (126.2-137.7)  87  19.7 (0.0-39.3)  13  151.6  36.9 (19.7-54.1)  A4  3  19.4 (0.0-31.2)  100  0.0  0  19.4  27.4 (18.0-32.8)  Average (SE*)  4  98.9 (58.7*)  78  28.7 (42.9*)  22  127.6  20.3 (15.8*)  SE=Standard Error  66  The separation of the stubble into the three major components also indicates the potentially large impact of mosses in increasing aboveground C in storage. The dead material predominantly represented the material left on-site following harvesting which had not been baled. It was included in any of the calculations as it was included during sampling. In Table 3.6, the range in the data at each site were included as it indicates not only the variation within a field but, as well, the absence of some components. This would not be indicated through the standard deviation statistic. The amount of stubble measured at Site A4 was substantially lower than at the other sites. This can be attributed to a combination of the limitations of the sampling method and the absence of weedy or moss material between the hay plants. Ideally, as hay plants grow in separate clumps, the assessment of the amount of stubble could be improved by excavating all plants, for example, over a metre square area. In this study, the cores were not centred on the plants. Efforts were made to place the cores half on the plants and half on the space between the plants except if the spaces between represented proportionally more of the area than the hay plants. Averaged over all sites, approximately 78% of the C in storage in the aboveground biomass was live stubble, with 22% in mosses. The amount representing long-term C storage averaged approximately 128 g C m" . Thus, C in long-term storage can be increased by permitting weeds to proliferate. However, a weed-free system resulted in greater NPP during the growing season. The NPP of the highly managed system was 535 g C m^yr" with 1  20 g C m" in storage. On the less managed sites, the NPP was 310 g C m" yr" with 125 g m " 2  2  1  2  of C in long-term C storage aboveground (average of Sites Al, A2, and A3).  67  3.3.3  Urban Land Use  3.3.3.1 Lawn The aboveground NPP of lawn cover for the 10 sites ranged from approximately 15 g C m" yr" to 145 g C m" yr' (Table 3.7). 2  1  2  1  Table 3.7 Aboveground N P P carbon of lawn. Site Ul U2 U3 U4 U5 U6 U7 U8 U9 U10  Front (gCm'yr ) 1  19.6 103.0 114.6 24.5 23.3 107.9  Back (gCm yf') 2  19.8 45.0  Plot (gCm yr') 2  143.9 58.7  16.6 68.5 124.6  70.0 36.3 30.8 (O*) 3.8 (F*)  Park Average (SE)* (O): open; (F): forested; SE=standard error.  Average Fertilized (gCm yr') 2  19.8 74.0 129.3 41.6 23.3 107.9 16.6 68.5 97.3 36.3 30.8  May (front) May (front)  June, July, Aug., Sept. June, July, Sept. April (front)  58.7 (39.2)  The greatest production occurred on one of the plots located in an open area of the backyard (Site U3). The 1-m x 1-m plots were generally cut on a monthly basis to allow the grasses to grow tall enough to collect clippings. Short clippings fell between the plants. The greater NPP on the plot compared to the mowed area was likely due to the frequency of clipping. The plot grasses were cut less frequently than the mowed areas in order to achieve height to collect the sample. Thus, the plot grasses had a greater opportunity to reach maximum leaf area index (LAI), intercepting more light than grasses cut before reaching  68  the maximum LAI (Madison, 1971). The plot areas also may have been less susceptible to droughty conditions. Lower mowing heights increase the susceptibility of grasses to heat and drought stress (Salaiz et al, 1995). This can be a factor in the study area as watering is not a common practice and droughty conditions and higher temperatures can occur particularly in the months of July and August. Frequent mowing results in a decrease in food reserves in the plant and in root production, reducing the plants capacity to absorb nutrients and water from the soil (Madison, 1971) and thus, production. Higher production occurred on the more intensively managed sites. Generally, front lawns were more intensively managed than back lawns (Table 3.7). This indicates that C assimilation can be increased through management. Turf grasses respond to high levels of nitrogen fertilization as this increases the rate of production of new leaves which are more photosynthetically active than older leaves (Turgeon, 1996). Lawn areas of higher production were frequently more intensively managed in terms of other practices. Personal observation and records supplied by the residents indicated that residents did not follow a standard program of lawn management such as rates and timing of applications of amendments or the adoption of various practices such as dethatching. Some residents applied lime and applied moss killer and herbicides. These practices occurred on random dates and not all practices were carried out uniformly. Records and personal communication indicated that little inputs if any were applied to sites with low production. Sites with lower production also occurred in shady areas such as at Site U5, which had a high moss cover, and at Site U7, where the lawns were dominated by weedy species. Frequently there was variability in the quality of the lawn over a site. The NPP of lawn in the study area was much lower than that reported by Falk (1980), which averaged 730 g Cm" . His study was carried out on two lawn areas in Washington, D.C. 2  One that was highly managed and the other generally left unmanaged. In the study by Falk 69  (1980), both lawns were cut to 5 cm height, which may explain the higher production in comparison to the findings of this study. Falk (1980) noted that other researchers frequently underestimate lawn productivity as they do not include all factors. Falk (1980) included live stubble sampled regularly throughout the growing season. As production decreases with a decrease in mowing height, it follows that the inclusion of the stubble would result in an overestimation of production. If the grasses were clipped to the soil surface at the end of the growing season, then the stubble could be considered in the NPP calculation. However, the grasses found in the lawn areas are likely perennials and the cool season grasses in the study area enter a semi-dormant state and often die during the winter. The leaves grow from the stems in the spring (Emmons, 1984). These dead leaves would be collected in the first cutting of the growing season. It would appear then that the stubble should be considered as C in storage and not be included in the production data. Falk (1976) also included a factor for turnover of the stubble. If the stubble turns over during the growing season, this should be reflected in the dead material in the grass clippings in our study collected throughout the growing season. Turgeon (1996) describes leaf production and notes that a turfgrass leaf undergoes senescence beginning at the tip and extending downward and falls away from the shoot. The number of leaves per shoot generally remains constant with the rate of leaf emergence approximately equaling leaf dieback. Turgeon (1996) notes that this occurs over a period of days. The dead material was included in the clippings collected for our study and as C has been expressed on a dry weight basis, it would seem that the biomass resulting from the turnover of stubble would be included in the dead material in the clippings. It is likely that if some of the leaves are dying back, that they may not be included in the clippings if they fell between the plants. They would remain on the ground surface and be  70  accounted for as the litter left on the surface at the end of the growing season. Material lost to decomposition during the growing season would not be included. In order to have a complete evaluation on the amount of C assimilated, it would be necessary to collect all grass litter that would originate from dieback. This perhaps could be carried out on a weekly basis as dead material on the surface would likely decompose rapidly during the growing season, especially when the moisture and temperature levels are suitable for microbial decomposition. For this study, stubble sampling was carried out at the end of the growing season and the dead grass litter was separated from the stubble. Nine cores were taken. Of the nine cores, four had no dead grass litter. Over the nine cores, dead grass litter averaged 5.2 g C m", with a maximum of 12.7 g C m" (Table 3.8). Falk (1976) 2  2  measured monthly standing dead biomass at <0.13 g C m". Averaged over a growing season 2  (May to September), this would represent approximately 0.65 g C m", i.e., less than 1 g C m" 2  2  of dead biomass for the growing season which would not have been accounted for if lost to decomposition during the growing season.  ^ Table 3.8 Aboveground carbon storage in lawn. Site  Stubble gCm 2  Ul U3- front U3-plot U4-front U4-back U9-front  10.8 224.8 358.5 23.0 20.3 91.0  %  U9-back 114.3 UlO-plot 51.2 Park-open 27.4 102.4 Average (SE) (117.6) Park-open: approximately 60%  5 95 100 20 5 80  Moss gCm 2  Total Dead Grass % (X C/m2) gCm 2  90 0 0 75 95 20  204.4 224.8 385.5 109.1 405.7 113.7  10.8 11.8 0.0 5.7 0.0 0.0  5 5 0 5 0 0  0.0 0 51.2 50 82.2* 75 91.3 (126.7) of biomass is due to weeds  114.3 102.4 0 193.7  12.7 0.0 5.5 5.2 (5.5)  10 0 5  90 50 25  193.6 0.0 0.0 86.1 385.4 22.7  %  71  As the range in production of grass clippings was variable across the sites and the •y  amount of dead litter averaged 5.2 g C m" for the cores sampled, an average of the aboveground NPP assimilated in lawns areas, not including the forest plot, has been calculated. This is based on the average for all sites with an additional 5.2 g C m" added for the dead litter. 2  Thus, the average NPP is 63.9 g C m" yr" (58.66 g + 5.2 g C m" yr"). 2  1  2  1  Carbon in storage in the stubble ranged from 10.8 g C m" to 358.5 g C m" with an 2  2  average of 102.4 g C m" (SD: 117.6) (Table 3.8). The large standard deviations (SD) are not only a reflection of the small sample size but also indicate the kinds of variability that occur as a result of different management practices and the effects of an uncontrolled experiment. Biomass at the surface included not only turf grasses but also mosses and weeds. Moss material averaged 91.3 g C m" over all sites. Two sites had greater than 90% moss in the 2  stubble samples while other sites had no moss. Approximately 60% of the stubble at Larch Park - open site were composed of weeds and 15% was moss. At Site U3- plot, 100%) of the sample was stubble, while at Sites U4-back and Ul-front only 5% of the sample was stubble. The highest amount of grass stubble occurred on sites which were more intensively managed, such as at Sites U3 and U9. Sites with the highest productivity had the highest stubble biomass (Table 3.7). This indicates how C in storage can be increased through management. This is different from the hay crop. In a turf system, the plants are close together and, if highly managed, there is no space for weeds to get established. As cores were collected from both highly managed and less managed sites, an average of the values should be representative of the study area. The data indicate that mosses can be responsible for storing a relatively large amount of C aboveground. The amount of C in storage in the stubble is assumed to be relatively constant throughout the growing season. This is partly related to the fact that the lawns in the study area 72  are not mowed in the winter, and to the fact that the frequency of mowing is relatively constant throughout the growing season. This is also based on the observation that the number of leaves per shoot generally remains constant under a specific set of environmental conditions (Turgeon, 1996). Further, the plant population density increases with an increase in mowing frequency (Madison, 1971). At the same time, yield decreases. As the owners in the study area generally had a set mowing schedule, the plant population density should have been similar throughout the growing season, on a site by site basis. 3.3.3.2 Rhododendrons - Aboveground  Carbon in storage is the biomass remaining on the plant at the end of the growing season. Therefore it includes all the aboveground components of the rhododendrons except the buds/flowers. The flowers fall to the ground following blooming and residents generally remove them, therefore, they are not part of shrub long-term C in storage. The leaves represent two years of storage in that it was noted that the current growing season stems are green and formed of soft tissue which becomes woody by the third year. By this time, the older leaves have dropped due to increasing shade. However, leaves remain on the shrubs throughout the year, representing an on-going rotation of this part of the plant. Therefore, leaves are included in long-term storage. As long-term C in storage is related to the biomass of the current season's stems, leaves, and buds (Whittaker, 1962) (Table 3.2), the biomass of these components was calculated to estimate the C stored in the branches, stems, and old leaves (Table 3.9).  73  Table 3.9 Short - and long-term aboveground carbon in storage in rhododendrons.* S h o r t - a n d L o n g T e r m A b o v e g r o u n d C i n Storage Age  Sample  (years)  Total C  Number  gm'  Wood  gin  %  2  O l d Leaves  gm  %  2  C u r r e n t Stems, Leaves, Buds gin  %  2  2  10  4  1,375  27  365  59  815  14  195  15  2 3 1  70 70 70  3,255 4,275 4,590  20 20 20  930  20 30  4,650 6,110 6,560  10 10 10  465 610 655  1,220 1,310  * Interpret data with caution -see text below  The amount of current year stem and leaf biomass could be determined from the biomass of current leaves, buds, and stems which were weighed separately. The stems averaged 0.46 g biomass (SE=0.20), the buds, 0.44 g (SE=0.16), and the leaf clusters, 1.94 g (SE=0.56). These values were included with that of the wood and old leaves to provide an estimate of the long-term C storage (Table 3.10).  Table 3.10 Long-term aboveground carbon in storage in rhododendrons*. L o n g T e r m C i n Storage Sample Number  Age  Total C gCm"  years  Wood %  2  C u r r e n t Stems and Leaves  O l d Leaves  g C m  g C m  815  % 11  3,255  930  8  350  1,220  8  460  1,310  8  490  4  1,325  28  365  15  2  4,535  71  %  gCm  61 21  2  10 20  3  5,955  71  4,275  21  30  1  6,395  71  4,590  21  2  2  145  * Interpret data with caution -see text below  The long-term C stored in the aboveground portion of the rhododendrons ranged from approximately 1,300 g C g m" for 10-year-old shrubs to 6,400 g C m" for 30-year-old shrubs 2  2  (Table 3.10). The woody component of younger shrubs represent only 30% of the aboveground biomass while current leaves, buds, and stems represent 11% of the shrub biomass, and old leaves, 61%. The high non-woody biomass in the younger shrubs is likely the  74  result of the shrub retaining several years of leaves as the plants have not reached full "canopy" closure. Therefore, the old leaves remain on the shrubs. As the shrubs mature, the proportion of C in the wood component represents 70% of total aboveground C. The calculations in Tables 3.9 and 3.10 are based on relationships reported by Whittaker (1962). This was the only data available on rhododendrons. This data needs to be interpreted with caution. When the NPP, which is also based on Whittaker (1962) (Table 3.11) is summed over the ages of the rhododendrons, it far exceeds the C in storage estimated. Research should be conducted on carbon allocation to make this relationship more reasonable. The amount of C stored in rhododendrons increased with age of the shrub. Carbon in storage increased substantially between 10 and 15 years. It follows a traditional sigmodial growth trend with the rate of biomass accumulation flattening as the plant matures (Figure 3.2).  10  15  20  30  Age (years)  Figure 3.2 Average aboveground carbon in storage of rhododendrons* in Abbotsford, B.C. * This data should be interpreted with cauction.  75  Similarly, as the C in storage was estimated, the NPP for the shrubs was estimated (Table 3.11), using the NPP percentages developed by Whittaker (1962) for the various components (Table 3.2).  Table 3.11 Aboveground N P P carbon in rhododendrons.* NPP-C Wood  Age  Sample  Total C  (years)  Number  g m" yr" 2  1  %  gm" yr" 2  1  Old Leaves %  gm" yr" 2  1  Current Stems, Leaves, Buds gm" yr" % 2  1  10  4  355  25  90  20  70  55  195  15  2  1,060  44  465  12  125  44  465  20  3  1,390  44  610  12  165  44  610  30  1  1,490  44  655  12  180  44  655  * This data should be interpreted with caution -see text above.  The NPP ranged from 355 g C m" yr" for the 10-year-old shrubs to 1,490 g C m" yr" 2  1  2  1  for the 30-year-old shrubs. Similar to the C in storage, NPP increased with the age of the shrub, substantially between 10 and 15 years, likely as a result of attaining full LAI. That is, the surface area occupied by leafy tips had been maximized resulting in an increase in the amount of photosynthesis occurring in the plant. The NPP then, following a traditional sigmodial growth trend, flattened as the plants matured (Figure 3.3). This suggests that maximum C assimilation rate occurs at approximately 20 years. Selected shrubs were re-sampled in the winter of 1997, reflecting the growth that had occurred in the 1996-growing season. Growth between the two years was compared. Average biomass of new stems increased from 0.46 g per unit stem in 1995, to 0.77 g per unit stem in  76  1996 representing an increase of 67% in biomass. The biomass of the buds similarly was greater in the 1996-growing season than in the 1995 growing season, increasing from 0.44 g per unit bud to 0.71 g per unit bud, an increase of 61%. This pattern was not evident in the leaf component with 1.94 g per unit growing tip in the 1995-growing season, to 1.96 g per unit growing tip in the 1996-growing season. This indicates that the stems and/or buds are similar in biomass accumulation and reflect growing season effects. Growing season effects did not affect leaf biomass per growing tip. Therefore, clipping of stems or buds may be a means of tracking growing season effects.  Carbon (g m^yr ) 1  10  15  20  30  Age (years)  Figure 3.3 Average aboveground NPP of rhododendrons in Abbotsford, B.C.* * This data should be interpreted with caution- see text above.  3.3.3.3  Annuals  The results of the measurements indicate the geraniums assimilated, aboveground, an average of 19.6 g C per plant (SD= 5.7) (oven-dried basis). On an area basis, geraniums assimilate 82.4 g C m" in the aboveground portion. The aboveground portion represented 87% 2  of the C assimilated. Photo 3.4 is typical of the geraniums plants excavated. 77  The C in geranium plants is assimilated during the growing season. The plants are excavated and generally composted at the end of the season. Therefore, they do not represent long-term C in storage. Once composted, organic matter from these plants is added to the soil. This provides new C and nutrients for the annual plants of the next growing season. Through this cycle, annuals contribute to C storage in the soil. Therefore, annual plants assimilate C during the growing season from the atmosphere and, following decomposition, contribute to the C in storage in the soil. By this, they contribute to the C sink. 3.3.4 Roots 3.3.4.1  Trees  3.3.4.1.1 Coarse Roots The data on coarse roots were estimated from studies of Douglas-fir in the Pacific Northwest and southwestern B.C. The literature review indicates that C in coarse roots of Douglas-fir trees increases with age (Sanantonio et al, 1987; Harmon et al, 1990b). Keyes and Grier (1981) estimated coarse roots on 40-year-old Douglas-fir stands at 3,270 gCra" which represents an average of the data collected from high and low productivity sites. Sollins et al (1980), in their studies of old-growth forest in the Pacific Northwest, estimated coarse roots at 7,065 g C m". Kurz (1989) found coarse roots averaged 3,700 g C m" on stands 2  2  ranging in age from 32 to 70years in southwestern B.C. Carbon in coarse roots is also affected by site conditions. Keyes and Grier (1981) found that coarse root biomass was 77% greater on a highly productive second growth site compared to that on a low productivity second growth Douglas-fir site.  78  With consideration o f the stand ages, site conditions, and the range o f results o f the studies reviewed, coarse root biomass has been estimated at 3,270 g C m" . This is based on the 2  findings o f Keyes and Grier (1981) as our study sites have been estimated to represent an average o f the two sites studied by Keyes and Grier (1981). Keyes and Grier (1981) also estimated the N P P o f coarse roots on the high and low productivity 40-year-old Douglas-fir stands at 80 g C m" yr" for the high productivity sties, 2  2  1  2  1  1  and 55 g C m" yr" for the low productivity sites, averaging approximately 70 g C m" yr" . They used logarithmic regressions o f tree dry weight on stem diameter. K u r z (1989) estimated coarse root N P P as approximately 14% o f aboveground N P P . Based on the aboveground data 9  1  of his study, coarse root N P P averaged 72 g C m" yr" for the range o f stand ages. A s our site has been estimated to represent an average o f the two sites studied by Keyes and Grier (1981), coarse root N P P for our study area has been estimated at 70 g C m" yr" , also similar to the 2  1  average o f the stands studied by K u r z (1989). 3.3.4.1.2 Small Roots Keyes and Grier (1981) estimated small root biomass for 40-year-old Douglas-fir sites in the Pacific Northwest at 100 g C m" . K u r z (1989) measured small roots o f Douglas-fir trees 2  on the same study discussed above. He found C ranged from 30 g C m" to 205 g C m" , 2  2  averaging 115 g C m" , similar to the average finding o f Keyes and Grier (1981). For the purposes o f this study, small root biomass has been estimated at 100 g C m" . 2  Little information is available on small root production. In the same study as discussed above, Keyes and Grier (1981) estimated small root production at 70 g C m" yr" on the low 2  9  t  1  9  1  productivity site and 55 g C m" yr" on the high productivity site, averaging 63 g C m" yr" . 2 1 2 1 K u r z (1989) estimated small root production ranging from 25 g C m" yr" to 110 g C m" yr" ,  79  2  1  averaging 68 g C m" yr" , again similar to the average found by Keyes and Grier (1981). The average of 63 g C m" yr" has been used for the C budget of our study. A comparison between the amount of C in storage at 100 g C m" and the NPP of 2  63 g C m" yr" indicates small root dieback. Small root production approximately equaled 2  1  mortality in second growth Douglas-fir stands on Vancouver Island (Kurz, 1989). This indicates that a large percentage of the small root biomass produced is not transferred to the small root long-term C storage pool. 3.3.4.1.3 Fine R o o t s  In the forested sites,fineroots averaged 187 g C m" in the 0-20 cm depth. 2  Approximately 65% or 119.3 g C m" offineroot biomass occurred in the 0-10 cm depth and 2  35% or 67.7 g C m" occurred in the 10-20cm depth (Table 3.12). 2  Table 3.12 Carbon stored in tree fine roots. Site  D e p t h : 0-10 c m gCm" [population] (SD) 2  Fl F2 F3 Average  117.9 [3] (40.5) 86.0 [5] (51.1) 149.4[3] (139.9) 119  D e p t h : 10-20 c m %of total  53 69 71 64  gCm" [population] (SD) 2  D e p t h : 0-20 c m %of total  gCm"  2  103.8 [4] (49.0) 38.1 [2]  47  221.7  31  124.1  61.1 (23.4) 67.7  29  210.5  36  186.7  The root data represent both live and dead roots. Fine roots of both trees and grasses are subject to dieback under severe environmental conditions, such as moisture stress (Kurz, 1989; Walton, 1983; Turgeon, 1996). Fine root production has been found to approximately equal mortality in second growth Douglas-fir stands (Kurz, 1989; Vogt et al, 1983). The  80  susceptibility of fine roots to dieback has been documented by Keyes and Grier (1981). They found that fine root biomass represented 8% of total root biomass but 57% of total root production. Conversely, the coarse roots represented 90% of root biomass and only 23 % of total root production. This indicates a large percentage of the fine root biomass produced is not transferred to long-term C storage. Even though coarse roots represent a much smaller proportion of total root production, almost all C in long-term storage in roots occurs in the coarse roots. Keyes and Grier (1981) reported that the NPP of fine roots was 140 g C m" yr" while 2  1  total fine root C in storage was measured at 220 g C m", similar to the fine root biomass of the 2  forested sites in our study area. Based on the findings of Vogt et al. (1983), Kurz (1989), and Keyes and Grier (1981), it is assumed that approximately 50% of the root biomass is live and 50%) is dead. It is not of critical importance if the roots were separated into live and dead components as the primary objective of this part of the project was to measure the amount of fine root biomass to establish a C budget using the same methodology as on the agricultural and urban soils. Because of the difficulty of measuring the proportion of root material that is dead or decomposing which is affected by the sampling period, root data are also subject to skepticism (Kurz and Kimmins, 1987; Santantonio and Grace, 1987). Despite these factors, the amount of fine roots in the soils in the forested sites in our study area were measured at approximately 9  9  185 g C m" , is within the ranges found by Keyes and Grier (1981) at 220 g C m" . It is also in the range found by Kurz (1989) at 195 g C m" and also by Vogt et al. (1983) at 94 g C m". 2  2  In terms of the NPP offineroots, Kurz (1989) estimated NPP from approximately 45 g C m" yr" to 205 g C m" yr", averaging 125 g C m" yr". Mortality ranged from 2  1  2  1  2  1  81  40 g C m" yr" to 205 g C m" yr", averaging 125 g C m" yr". Kurz (1989) estimated that 2  1  2  1  2  1  production represented 64% of storage. Keyes and Grier (1981) estimatedfineroot production between 235 g C m" yr" in the low productivity site and approximately 60 g C m" yr" in the 2  1  2  1  high productivity site, averaging 145 g C m" yr". Keyes and Grier (1981) estimatedfineroot 2  1  production at 63% offineroot biomass as did Kurz (1989). These values are very similar considering the subjective nature of the techniques used and the fact that the studies were carried out at different sites. Based on these findings,fineroot production was estimated at 63% of 185 g C m" or 117 g C m" yr". This is within the range found by Kurz (1989) and 2  2  1  Keyes and Grier (1981). 3.3.4.2 Understory  Although aboveground measurements of understory under Douglas-fir forests in the Pacific Northwest have been carried out (Gower and Grier, 1989; Turner and Long, 1975; Cole et al, 1967), belowground components have not been investigated. The roots of understory have been assumed to be equal to aboveground biomass. As the estimate for aboveground 9  9  understory was 115 g C m" , root biomass is estimated at 115 g C m" . Even if this has been overestimated by 100%, the amount of carbon in error represents only 57 g C m". The low 2  amount of carbon predicted in understory roots may be the reason for the lack of study of this component. 9  1  The aboveground NPP for this study was estimated at 25 g C m" yr" . Therefore the 9  1  NPP of understory roots has been estimated at 25 g C m" yr" . 3.3.4.3 Hay  Fine root biomass of the hay averaged 151 g C m" in the 0-20-cm depth (Table 3.13). It ranged from 117.2 g C m" to 150.7 g C m" in the 0-10-cm depth, averaging 138 g C m". It 2  2  2  82  ranged from 79 . g C m" to 15.2 g C m" in the 10-20-cm depth, averaging 12.4 g C m". 2  2  2  Approximately 92% of root biomass occurred in the 0-10 cm depth. Greater root biomass in the shallower depth was predicted as root systems in many grass species are relatively shallow rooted (Walton, 1983).  Table 3.13 Carbon stored in hay roots. Site  Sample Number  Al  2  A2  2  A3  3  A4  3  Average  4  Depth: 0-10 cm gCm (range) 150.1 (144.4-155.9) 141.5 (134.9-148.0) 117.2 (75.6-145.5) 144.4 (122.8-161.2) 2  138.3  Depth: 10-20 cm % 93 90 89 95  Depth: 0-20 cm gCm"  gCm" (range) 12.1 (4.7-19.4) 15.2 (10.5-20.0) 14.4 (10.0-22.6) 7.9 (2.6-15.2)  %  11 5  152.3  12.4  8  150.7  92  2  2  7  162.2  10  156.7 131.6  Root growth is negatively affected by insufficient nutrient availability (Walton, 1983), it was expected that fine root biomass would be substantially greater at Sites A2 and A4 as these sites received manure applications. However, the highest root biomass occurred at Site A l and this can possibly be attributed to a high weed presence that occurred between the hay plants. This site had a lower aboveground productivity than Sites A2 and A4 (Table 3.5) but had the highest amount of stubble (Table 3.6). Root growth is at the lowest rate during the summer months (Walton, 1983). This is expected as fine roots suffer dieback under conditions of high temperatures and a soil moisture deficit (Walton, 1983). The maximum growth rate of roots occurs during the cool weather in the early spring. The sampling for roots occurred in the late winter-early spring while the roots 83  were dormant. Therefore, less root biomass could be expected during the late winter-early spring than in spring/early summer, after the roots had sufficient time to divide and elongate. The NPP of the fine roots is assumed to be similar to the amount of fine root biomass measured. This is the result of on-going production and dieback of fine roots (Walton, 1983). Because of fine root dieback, similar to what occurs for trees, production likely occurs at a relative steady state with no major long-term accumulation occurring in the fine root component. As the NPP of forest fine roots is considered to represent 64% of fine root biomass, the estimate of the NPP of hay fine roots was also based on it being 64%> of fine root biomass. It was therefore estimated at 96 g C m" yr". 2  1  3.3.4.4 Lawn  Root sampling was carried out at selected sites in the lawn areas. Root C averaged 79 g C m" for the 0-20-cm depth (Table 3.14). It averaged 69 g C m" in the 0-10-cm depth, 2  2  ranging from 21 g C m' at Site U7 to 142 g C m" at U8. It ranged from 2.3 g C m" at Larch 2  2  2  Park to 28.0 g C m" at U2-front in the 10-20-cm depth, averaging 13.0 g C m". 2  2  Table 3.14 Carbon stored in lawn roots. Site  Location  Depth: 0-10 cm gCm"  U2 U3 U4 U7 U8 Park Average (SE)  front back front plot front back front Front plot-open  2  70.0 60.7 74.7 91.0 74.7 46.7 21.0 142.3 42.8 69.3 (34.4)  Depth: 10-20 cm % 71 84 80 89 89 83  95 84  gCm"  2  28.0 11.7 18.7 11.5 9.3 9.3  2.3 13.0 (8.2)  % 29 16 20 11 11 17  5 16  Depth: 0-20 cm gCm"  2  98.0 72.4 93.4 102.5 84.0 56.0  45.1 78.8  84  Approximately five times greater root biomass occurred in the 0-10 cm depth compared to the 10-20cm depth. Falk (1976) also found that fine roots were concentrated near the surface, reporting that nearly 100% of turf roots occur in the upper 7.5 cm. Emmons (1984), however, notes that turf grass roots are concentrated in the top 15- to 30 cm of the soil. The higher amounts of root biomass were found on the more intensively managed sites and the lowest root biomass occurred on the least managed sites. The type of grass also affects the amount of biomass in the roots. The amount of C in turf roots measured on a California site, in the spring, was 364 g C m" (Falk, 1976). The higher root biomass may be due to the warmer climatic conditions as the roots of warm season grasses are thicker than cold season grasses (Emmons, 1984). Sampling time is also important when measuring turf grass roots. Similar to hay and tree fine roots, cool-season turfgrasses are susceptible to midsummer stress periods which may result in root death (Turgeon, 1996). As the cores were taken at the end of the growing season, the grasses may have been subject to root dieback as watering is not a common practice in the study area. Similar results were found in studies in Michigan, U.S.A., where the greatest root biomass occurred early in the growing season followed by a decline in July Murphy et al, 1994). Most turf grass roots remain alive for six months to two years (Emmons, 1984), replacing most of their roots systems each year (Turgeon, 1996). Maximum root production likely occurs in spring and early summer in our study area, when soil moisture is higher and temperatures are lower. Therefore, the roots were likely sampled at a period of relatively low biomass. Carbon sequestered in the growing season in roots is generally not converted to C in storage in roots because of dieback. With dieback and replacement on an annual basis, the amount of C in roots remaining in the soil during the winter likely is similar to the amount of C 85  stored in the roots throughout the year, the exception being during the months of May and June when root initiation and growth of cool season grasses are highest (Turgeon, 1996). Root production is also affected by mowing height and frequency of cutting. Salaiz et al. (1995) found root production increased with mowing height. Therefore, plots such as those used in this study, which were cut on a monthly basis, would result in greater production than if clipped weekly. The plot at Site U3 had the highest NPP in terms of the grass clippings (Table 3.7) and the highest amount of stubble (Table 3.8). It also had a relatively high amount of C (102.5 g Cm") stored in thefineroots. Since it was cut less frequently, less depletion of the 2  carbohydrate reserves likely occurred in the roots compared with the lawns that were cut more frequently. When a grass plant is cut, the manufacturing of carbohydrates is reduced. Root growth is slowed or stopped if nutrient supplies to the roots are diminished (Madison, 1971). The range in the amount of C sequestered in thefineroots in lawn in our study, indicates the effects of various management practices. As well, the highest root biomass (Table 3.14) occurred on the sites with the greatest amount of live stubble (Table 3.8) and the highest productivity (Table 3.7). This indicates C storage and assimilation rates in lawn are greatly affected, and can be increased, by management. 3.3.4.5 Rhododendrons  Little information exists on the roots of rhododendrons. Yarie (1978), Telfer (1969), and Means et al. (1994) only considered aboveground biomass and production of rhododendrons. Whittaker (1962), however, excavated the roots of three rhododendrons, an 11-year-old shrub, a 16-year-old, and a 40-year-old and determined the root to woody shoot ratio based on age. The root-woody shoot ratio was estimated at 1.62 for the 11-year-old shrub, 0.85 for the 16-year-old shrub, and 0.75 for the 40-year-old shrub. This indicated that the  86  major accumulation of biomass in the young shrub occurs in the root system. With age, the greater accumulation of biomass occurs in the aboveground portion of the plant. The estimation of the root biomass of the rhododendrons was based on these ratios developed by Whittaker (1962). The ratio of 1.62 was used for the 10-year-old category and an average value of 0.80 was used for the remaining shrubs (Table 3.15). The ratios suggest that roots represent a major C storage pool in rhododendron budgets. Estimated root biomass ranged from 590 g C m" for a 10-year-old shrub, to 3,675 g C m" for a 2  2  30-year-old shrub (Table 3.15). Whittaker (1962) did not separate the roots into fine, small, and coarse. As rhododendrons are woody shrubs which can grow to tree heights, e.g. 4 m, it is assumed that the roots include a coarse root (>5 mm diameter), a small root, and a fine root component such as occurs with trees. For comparison purposes, a first approximation has been carried out on the proportions of the different sized root fractions (Table 3.15). (Separation into size fractions affects predicted NPP rates but not thefinalroot biomass). This data should be interpreted with caution. See previous comments under Section 3.3.3.2.  Table 3.15 Carbon stored in rhododendron roots.* Age  Woody Shrub  Years  gCm"  Root/ Shoot Ratio  Roots gCm"  2  Coarse Roots 2  10  365  1.62  590  15  3,255  0.80  2,605  20  4,275  0.80  3,420  30  4,590  0.80  3,675  Small Roots  Fine Roots  gCm" (% of total)  gCm' (% of total)  gCm" (% of total)  30 (5%) 340 (13%) 855 (25%) 920 (25%)  130 (22%) 705 (27%) 855 (25%) 1,100 (30%)  430 (73%) 1,560 (60%) 1,710 (50%) 1,655 (45%)  2  2  2  * This data should be interpreted with caution.  87  The estimation of the coarse root component was based indirectly on the pattern of root growth on Douglas-fir trees and thus should be interpreted with caution. With woody stems and branches and heights of four metres, rhododendrons can be considered as small evergreen trees. The aboveground structure of a rhododendron is different from that of a Douglas-fir tree in that the biomass of the rhododendron is spread throughout the shrub. There is no main stem. The biomass of a tree is supported by a single stem. Therefore the proportion of coarse to small and fine roots should be higher in a tree than that required by the evenly branched rhododendron. The root system of a rhododendron has a ball structure while the root system of a Douglas-fir tree has a lateral structure to anchor it over a larger horizontal area. In a 40-yearold Douglas-fir tree, 90% of the C stored in the root system occurs in the coarse roots. The estimates of the coarse root component of the rhododendrons has been based on a comparison of the 20-year-old rhododendrons in the study area to the estimates prepared for the Douglas-fir trees, as both are considered to be near the top of the sigmoidal growth curves. The coarse root component of the Douglas-fir trees has been estimated at 20% of the aboveground woody biomass. Therefore, the value of 20% of the aboveground woody C has been used to estimate the amount of coarse roots in the 20-year-old rhododendrons. A different value was used for the 15- and 10-year-old shrubs. Research is required to confirm this assumption. Time is required for the growth of coarse roots. Therefore it is assumed that the coarse root component of the younger shrubs is less than in the mature plants. The coarse root component of a 15-year-old shrub was estimated at 10% of the aboveground woody component, and 5% of the aboveground biomass for a 10-year-old shrub. Rhododendrons, however, have proportionally greater root biomass than the Douglas-fir trees. Whittaker (1962) estimated that root biomass is 80% of woody aboveground shoot biomass in rhododendrons. The remainder of the root biomass was divided into small andfineroots. The assumption was 88  thatfineroots are more prolific in the younger shrubs (Table 3.15). As well, time is required to accumulate small roots. Based on these estimates, root biomass increases with shrub age, such that root biomass increased from 59 lg C m" for a 10-year-old shrub to approximately 2  3,400 g C m" for a 20-year-old shrub. 2  Whittaker (1962) did not provide data on the NPP of roots. In order to estimate the average NPP of coarse roots, the C in storage in the coarse root component was divided by the age of the shrub. The NPP of small andfineroots is based on a 50% mortality rate. The assumption was made that thefine(and small) roots of rhododendrons suffer die-back, as reported for Douglas-fir trees, hay, and turf grasses. Therefore, it was assumed that the fine and small roots of rhododendrons are also subject to mortality. The NPP estimates of rhododendron roots are presented in Table 3.16. Table 3.16 NPP of rhododendron roots.* Age  Coarse Roots  Total Roots  NPP g C m" yr" (% of total)  NPP g C m" yr" (% of total)  NPP g C m ' yr"  10 65 (23) 2(<D 23 (2) 345 (30) 15 420 (32) 20 40(3) 30 28(2) 545 (39) Interpret this data with caution.  215 (77) 780 (68) 855 (65) 825 (59)  280 1,150 1,315 1,400  NPP g C m" yr" (% of total) 2  years  •  Fine Roots  Small Roots  1  2  1  2  1  2  1  Less than 3% of total root production has been allocated to coarse roots across all age groups, with approximately 30% of NPP allocated to small roots, and over 65% allocated to fine root production.  89  3.3.4.6 Annuals  The results of the measurements indicate that the roots of the geraniums account for 3.0 g C per plant (SD=0.3). On a spatial basis, the geraniums assimilated 12.7 g C m~ in the 2  roots. A noted earlier, the aboveground portion represented 87% of the C assimilated with 13%> assimilated in the rooting zone. This can be seen in Photo 3.4. Carbon in roots of annuals is sequestered during the growing season. At the end of the season, the roots (and aboveground portion) are excavated and generally composted. Therefore, they do not represent C in long-term storage. Once composted, the organic matter from the roots is added to the soil to provide nutrients for the plants of the next growing season. Thus, the roots are part of a shorter C cycle. Through the addition of the C to the soil, they contribute to the long-term C storage pool. Annual plants can then be considered to sequester C during the growing season, from the atmosphere. Ultimately the sequestered C is transferred to the soil C storage pool.  3.3.5  Soils  3.3.5.1 Bulk Density  3.3.5.1.1 Forest The bulk density (Db) of the soils at the forested sites averaged 0.73 kg l" (Table 3.17). 1  The bulk density of the upper soil layer (0-10-cm depth) was 0.58 which is less than reported by Fried et al. (1990) for 35- to 60-year old Douglas-fir stands in western Oregon, U.S.A. This is also 35% less than the lower layer (10-20-cm depth) measured, which was 0.88 kg l" . 1  This lower Db in the 0-10-cm depth is attributed to the occurrence of fine roots. Sixty-five percent of thefineroot biomass occurred in the upper 10cm of the soil (Table 3.13).  90  Table 3.17 Bulk density (D ) of forest and agricultural (hay) soils. b  FOREST SD* N* (SE)*  AGRICULTURE SD* N* (Db) (SE)* (kgr )  Depth (cm  Site  0-10 0-10 0-10 0-10  FI F2 F3  0.51 0.60 0.63  0.13 0.10 0.20  4 3 3  Al A2 A3 A4  0.85 0.79 0.79 0.86  0.20 0.02 0.07 0.05  10-20 10-20 10-20 10-20  FI F2 F3  0.76 0.90 0.98  0.06 0.08 0.01  4 4 3  Al A2 A3 A4  1.05 0.95 0.90 1.05  0-10 10-20  Ave. Ave.  0.58 0.88  (0.06) (O.H)  Ave. Ave.  0.82 0.98  0.09 0.04 0.02 0.13 (0.04) (0.08)  (D ) b  (kgl ) 1  Site  1  2 2 3 3 2 2 2 3  0-20 Ave. 0.73 Ave. 0.90 *SD=standard deviation; SE=standard error; N=sample population  Low standard deviations at both depths indicate low variability in Db across the sites and low systematic error. The low standard error indicates the low variability between sites. 3.3.5.1.2 Agriculture (Hay)  Bulk densities of the agricultural (hay) soils averaged 0.90 kg l" in the 0-20-cm depth. 1  The Db of the lower layer (10-20 cm) was 20% higher than the upper layer (0-10 cm) (Table 3.17). The higher Db of the agricultural soils compared to the forested soils is attributed to compaction from harvesting and manure spreading equipment. Similar to the forested soils, low standard deviations at both depths indicate low variability in the Db across the fields and low systematic error. The low standard error indicates the low variability between sites and thus good replication. Comparisons in Db were made between the small and larger corers to correct for the use of the smaller corers on the urban sites. Bulk densities were 20% higher in the 0-10-cm depth using the 1.5-cm in diameter corer compared with the findings using the larger corer (10-cm diameter) (Table 3.18). The Db were 54% higher in the 10-20-cm depth using the 1.5-cm corer. 91  This indicates that the smaller corer overestimates Db likely due to compaction of the soils during insertion into the soil as a result of the greater influence of edge effects. As well, the error can be attributed to the difficulty of probing to the exact depth. The probe was 27 cm long and the sampling depth was 20 cm. (Tape was placed at 10- and 20 cm from the base of the probe as the depth marker). The fact that the Db was overestimated more in the deeper layer across all three sites suggests that even with the depth marked on the probe, the tendency was to push the probe too deep into the soil. The lower SD indicated that this was a consistent tendency. The large SE for the 0-10-cm depth suggests that error using the smaller probe varies depending on the land use. That is, the differences in the soil may affect the values obtained. This does not seem to be a factor with the standard (larger) bulk density sampler.  Table 3.18 Bulk density (Db) of 10-cm cores versus 1.5-cm cores on agricultural (hay) soils. Site  10-cm Corer Depth Site D„ (cm) (kgl ) 1  SD* (SE)*  N*  1.5-cm corer SD* Site D (SE)* (kgl ) b  N*  1  Al  0-10  0.85  0.20  2  0.69  0.01  2  A2  0-10  0.79  0.02  2  1.04  0.12  4  A3 A4  0-10 0-10  0.79  0.07  3  0.94  1  0.86  0.05  3  1.30  0.06  Al  10-20  1.05  0.09  2  1.50  0.08  3  A2  10-20  0.95  0.04  2  1.52  0.10  4  A3 A4  10-20  0.90  0.02  2  1.40  10-20  1.05  0.13  3  1.60  0.16  3  Ave.  0-10  0.82  (0.04)  0.99  (0.25)  Ave.  10-20  0.98  (0.07)  1.51  (0.08)  Ave.  0-20  0.90  3  1  1.25  *SD=standard deviation; SE=standard error; N=sample population  92  Standard deviations for both methods are low and similar for both methods indicating the 1.5-cm core method is as precise as the larger Db sampler. Separate regression analyses were carried out for the two depths to provide equations to correct for the use of the smaller probe. The following equations were developed from the regressions:  0-10-cm depth:  D (kg l") = 0.59 + 0.19 (SC) (kg l")  10-20-cm depth:  D (kg l") = 0.87 + 0.08 (SC) (kg f )  1  1  b  1  1  b  where SC stands for the Db obtained with the smaller corer.  To compare the Db over time, 1.5-cm cores were also collected in the spring of 1997 in the agricultural soils. The difference between the 1996 and 1997 sampling years amongst all four farms averaged less than 0.02 kg l" for the 0-10-cm depth and less than 0.08 kg l" for the 1  1  10-20-cm depth. The differences over the two years on all four farms (Table 3.19) were not significant using the Wilcoxon Matched-Pairs Signed-Ranks test. This suggests that the Db is relatively consistent on a site per site basis from one growing season to the next. The low SD for both years indicates that the use of the smaller corer yields consistent results.  93  Table 3.19 Bulk density ( D ) of 1.5-cm cores on agricultural (hay) soils 1996 vs. 1997. b  Site Al A2 A3 A4  Depth (cm) 0-10 0-10 0-10 0-10  Al A2 A3 A4 Ave. Ave.  10-20 10-20 10-20 10-20 0-10 10-20  Ave.  0-20  1996 Site D  b  (kgr ) 1  0.69 1.04 0.94 1.30 1.50 1.52 1.40 1.60 0.99 1.51 1.25  SD* (SE)* 0.01 0.12 0.06 0.08 0.10 0.16 (0.25) (0.08)  N*  Site D  b  (kg! ) 1  2 4 1 3 3 4 1 3  0.81 1.06 0.92 1.24 1.45 1.35 1.35 1.55 1.01 1.43  1997 SD* (SE)* 0.09 0.01 0.14 0.09 0.22 0.01 0.19 0.05 (0.19) (0.10)  N* 2 2 3 3 2 2 3 3  1.22  *SD=standard deviation; SE=standard error; N=sample population  The consistency of the 1.5-cm soil core data over the two sampling periods for the four farms suggests that a regression comparing the two methods is a feasible approach to adjusting the data collected with the 1.5-cm corers. 3.3.5.1.3 U r b a n (Lawn)  The average corrected Db for the lawn areas was 0.90 kg l" (for the 0-20 cm depth). 1  The bulk density was estimated at 0.79 kg l" for the 0-10-cm depth and 1.00 kg l" for the 101  1  20-cm depth. Little difference occurred between front and back yard Db in the 0-10-cm depth. However, in the 10-20-cm depth, the Db was greater in the front yards by 4% compared to the back yards.  94  3.3.5.2 Soil Carbon  3.3.5.2.1 Forest Carbon in the forest soils averaged 6,000 g C m" in the 0-20-cm depth (Table 3.20). Of 2  this, 65% or 3,800 g C m" occurred in the upper soil layer and 35%> or 2,000 g C m" occurred 2  2  in the lower layer (Table 3.20). This ratio was also found in soils under 20 to 60-year-old Douglas-fir stands in the Pacific Northwest (Ranger et al, 1995) and on hardwood forests (Knoepp and Swank, 1997). Table 3.20 Soil carbon in forest soils. Depth: SD* N* 10-20 cm (SE)* (gCm" ) FI 582 3 2,665 837 4 F2 271 4 1,475 173 4 960 112 F3 3 2,035 3 (750) 2,055 (597) Average 3,860 %= 35 %= 65 *SD=standard deviation; SE=standard error; N=sample population Site  Depth: 0-10 cm (gCm" ) 3,945 3,070 4,565  SD* (SE)*  N*  2  2  Depth: 0-20 cm (gCm ) 6,610 4,545 6,600 2  5,915 %=100  3.3.5.2.2 Agriculture (Hay) Carbon in the agricultural soils averaged 8,800 gCm' for the 0-20-cm depth. Approximately 60% of the soil C occurred in the upper layer and 40% occurred in the lower layer (Table 3.21). The higher C content of the upper layer was expected as the greater portion of fine roots also occurred in this layer. Some authors claim that soils can be managed for mitigating the greenhouse effect (Donigian et al, 1995; Lai et al, 1995; Li, 1995; Cole et al, 1993; Kern and Johnson, 1993). One accepted method is to increase soil C through the application of manure (Lai et al, 1995;  95  Stewart, 1993). The farmers at Sites A2 and A4 did report that they had previously applied manure. However, the effect of manure on soil C is not evident. Table 3.21 Soil carbon in agricultural (hay) soils.  Site  Depth: 0-10 cm (gCm ) 4,840 5,295 5,535 5,565 5,310 %=60  SD* (SE)*  N*  2  Al A2 A3 A4 Average  AGRICULTURE Depth: SD* (SE)* 10-20 cm (g C m" ) 3,840 1,130 3,445 270 3,835 891 2,825 990 3,485 (479) %= 40  N*  2  2  1,106 677 91 161 (335)  2 2 3 3  Depth: 0-20 cm (gCm" ) 8,680 8,740 9,370 8,390 8,795  2 2 3 3  SD=standard deviation; SE=standard error; N=sample population  Conservation tillage and no tillage practices also result in the accumulation of soil C and therefore are options considered in agriculture for mitigating the greenhouse effect (Kern and Johnson, 1993; Stewart, 1993). Growing hay, a perennial crop that does not require regular cultivation, can therefore be considered an agricultural activity that could be adopted as a means of supporting the maintenance of the soil C sink. 3.3.5.2.3 U r b a n  Carbon content of the lawn soils averaged 6,500 g C m" in the 0-20-cm depth. Approximately 60% of the soil C occurred in the upper layer and 40% in the lower layer (Table 3.22). The higher content of the shallower depth was again predicted as more roots were measured in the upper layer. Although the front yard lawn areas were managed more intensively than the back yards, this was not reflected in the soil C. There was no significant difference between the soil C between the front and back yards (Wilcoxon Matched-Pairs Signed-Ranks Test). However, variation did occur in soil C across the sites, particularly in the 0-10-cm layer. This variation in 96  the upper layer reflected management differences between sites. Carbon content of the soils at Site U3-front in the 0-10-cm depth was double the amount in the soils at Site U7. Site U3 was intensively managed with dethatching, liming and fertilizing. No management inputs were applied to the lawn at Site U7 which was predominantly moss covered. The difference is interpreted as reflecting the effects of fine roots on soil C. Fine root biomass was 3.5 times greater at Site U3 compared to that at Site U7 (Table 3.14). Table 3.22 Soil carbon in the urban soils. Site  Location  Depth: 0-10 cm (g C m" ) 2  U2 lawn U3 lawn U4 lawn U7 lawn U8 lawn Park lawn Average lawn Park forest Rhodo.  Depth: 10-20 cm  Depth: 0-20 cm  (g C in" )  %  (g C rn )  58 58 66 59 57 59  2,900 2,845 2,365 3,255 2,730 2,795  42 42 36 41 43 44  6,890 6,700 6,940 7,875 6,410 6,340  %  2  2  front back front back (plot) front back front  3,990 3,855 4,575 4,620 3,680 3,545 2,630  front  3,065  open  4,215  62  2,560  38  6,770  3,795  58  2,780  42  6,575  forest  4,785  RIO Rll  4,900 4,330  58  3,280  8,065  3,175  7,505  Other management techniques also result in increases in soil C. This is evident when comparing the front yard and plot of Site U3. Site U3-front was thatched and fertilized, however, the back yard did not receive this treatment, yet, soil C was higher in the plot area than in the front lawn area. This is attributed to the higher amount of fine roots in the plot, which exceeded the front area by 20%. This is considered to be a response to the less frequent  97  cutting of the grasses in the plot. It suggests that soil C can be increased through changing the mowing program. Several cores were collected in the forested area of the park. These were composited. The soils from the forested areas had 20% more C, at 8,000 g C m" averaged over the two 2  depths, compared to the park lawn soils. The grass in the forested portion of the park was sparse. Large twigs and branches were removed from the park but the needle litter was left and would have provided a source of C to the soils. The C content of the soils collected under two rhododendron plants was lower than the forest soils occurring in the park but higher than the lawn soils. 3.3.6  Land Use Versus Carbon Storage and Assimilation Rates - Summaries  3.3.6.1 Natural and Urban Forest Carbon Storage and NPP Budgets  The C budget of the forested sites has been prepared from the information previously presented on a component basis. Total C in storage in the natural forest has been estimated at 29,260 g C m" (Table 3.23). Carbon in the urban forest has been estimated at 28,000 g C m" 2  2  (Table 3.24). Table 3.23 Carbon budget of natural forests. Pool  Storage gCm 18,000 115 615 945 3,270 100 185 115 5,915 29,260 2  Aboveground tree Aboveground understory Coarse woody debris Litter Tree coarse roots Tree small roots Tree fine roots Understory roots Soils Total  % 62 <1 2 3 11 <1 <1 <1 20 100  NPP g C m" y r 525 25 1  % 64 3  70 65 115 25  8 8 14 3  825  100  2  98  Table 3.24 Carbon budget of urban forests. Pool  Storage gCm" % 64 18,000 3,270 12 100 <1 185 <1 6,575 23 28,130 100 2  Aboveground tree Tree coarse roots Tree small roots Tree fine roots Soils Total  NPP g C ni" y r 525 70 65 115  % 68 9 8 15  775  100  2  1  In the natural forest, there are nine separate C storage pools (Table 3.23). However, 82% of the C occurs in two pools: the aboveground portion of the trees which represents 62%> or 18,000 g C m" of the C in storage, and the soil which represents 20% or 5,915 g C m". 2  2  Coarse roots also represent a relatively large C pool at 11% or 3,200 g C rn". Coarse woody 2  debris and litter represent 5% of the total budget. Fine and small roots and the understory represent less than 1% of the C budget. Total NPP for the natural forest is estimated at 825g C m" yr". That is, approximately 2  1  825g C m" yr" is added to the forested ecosystem each year in the study area. Approximately 2  1  65%) of this occur in the aboveground component of the trees. Fine roots also contribute a large amount of C annually. Fine roots represent 14% of the total NPP of the forested system. As noted previously, however, fine (and small) roots are subject to on-going dieback. The low amount of C in storage in the fine roots indicates that the C assimilated in this pool is not transferred to the root storage pool and likely it is transferred to the soil pool. This is suggested by the finding that the fine root biomass and soil C were found to be highly correlated (96%). The natural forested sites include woody debris, litter and understory. The urban forest represents trees in an urban landscape. The coarse woody debris and litter are removed from  99  the sites. Lawn, shrubs, and gardens replace the understory. The number of trees occurring in an urban landscape varies depending on the preferences of the owners. Therefore, the C budget for the urban forest is based on data from the natural forest, exclusive of coarse woody debris, litter, and understory (Table 3.24). In the urban forest budget, the soil C was based on the C of the soils under lawn as the trees in the urban area were predominantly located within lawn areas. A comparison between the natural and urban forests budgets indicates that the natural forest has on the order of 1,000 g C m" more in storage than an urban forest. This is a result of 2  the additional pools in the natural forest. As the soil is generally not bare under trees in urban centres, the urban forest budget should be supplemented with the C budgets of the other vegetative types. This can be done when preparing a budget on a metre square basis. It allows the calculation of C based on the landscape material selected. It can also accommodate the inclusion of impermeable surfaces that commonly occur in urban systems. Urban trees may be subject to different management techniques than occur in natural forests. Trees in urban centres are frequently pruned at the base in order to reduce the amount of yard area occupied. The effects on C storage and assimilation rates of such management techniques are difficult to estimate as there is no set amount of material removed. Such techniques should result in a retardation of tree growth as removal of the foliage and branches results in a removal of photosynthetic surfaces. Keyes and Grier (1981) reported that the foliage and branches were responsible for approximately 30% of the aboveground NPP. Therefore, a removal of 20% of the foliage and branches, for example, would result in a decrease of approximately 6% of the total NPP of the aboveground portion of the tree. In terms of a loss of C in storage, Keyes and Grier (1981) report that foliage and branches represent approximately 10% of the C in the aboveground portion of 40- year-old Douglas-fir trees. Therefore, the amount of stored C lost through pruning could be potentially estimated by 100  adjusting the C budget of the tree by the amount of branches and foliage removed. For example, if 20% of the foliage was removed this would represent a reduction in the C stored aboveground of approximately 2%>. As trees occur as single entities in urban areas, calculation of the C budget of a tree poses a challenge. It is proposed that it be based on the stem area. Keyes and Grier (1981) estimate that the stemwood and bark represent 78% of the aboveground stored C. Douglas-fir trees are conical in shape with the diameter of the stem decreasing with height. The lower part of the stem stores a much higher portion of the stem C than the top of the tree. To accommodate a tree in an urban setting, it is proposed that the C budget be based on the square metres of the land area occupied by the stems of the trees measured at DBH. An assumption is made that the decrease in C in the stem at the top of the tree is balanced by the foliage, branches and roots which extend beyond the spatial area occupied by the stems. In preparing C budgets for natural forests in the Abbotsford area, focus should be on the aboveground tree pool and the soil as these two pools represent 82%> of the C budget. A value for the coarse root component should be included, if possible, as it represents 11% of the C budget. Under limited time or financial constraints, or to get afirstapproximation, the data collection program can focus on these three pools as they represent 93% of the total budget. In the urban forest, the focus should be on the aboveground portion of the tree, the coarse roots, and the soil. These three pools represent 99%> of the C budget. Although these budgets have been prepared for the Abbotsford area, the trends discussed above should be transferable to other temperate coniferous forests, natural and urban.  101  3.3.6.2 Carbon Storage and NPP Budgets-Hay  Carbon in storage under hay has been estimated at approximately 9,075 g C m". Of 2  this, 97% occurs in the soil (Table 3.25). The amount of C in the stubble represents only one percent and the roots only two percent of the total carbon budget. The hay grown during the summer was harvested and it represents the NPP. The average NPP for all sites was 490 g C m ' yr". The NPP aboveground represented 80% of the total NPP, ranging from 329 2  1  to 544 g C m" yr". Fine roots represented 20% of the total NPP at 96 g C m" yr". 2  1  2  1  Table 3.25 Carbon budget of land under hay production. Pool  Storage gCm" % 1 130 150 2 8,795 97 9,075 100 2  Aboveground Fine roots Soils Total  NPP g C m yr 395 95  80 20  490  100  2  1  %  In the hay systems, soil is the major C storage pool. Therefore, in preparing a C budget, focus can be on this pool. In hay operations, the NPP of the aboveground portion is an indicator of the role of management in C assimilation. When managing for the greenhouse effect, the goal is to maximize C O 2 sequestration from the atmosphere. The NPP of the aboveground portion will indicate if optimum management practices are being carried out for this purpose. 3.3.6.3 Carbon Storage and NPP Budget-Lawn  Similarly to the land under hay, approximately 96% of the C stored in a lawn system is in the soil and only 3% is stored aboveground (Table 3.26). Grass clippings produced throughout the growing season represent the NPP. The clippings are removed and generally  102  composted in the study area. Carbon in the composted organic matter is used in the gardens. It therefore contributes to the soil C pool in the garden areas. Table 3.26 Carbon budget of land under lawn. Pool  Storaf 'e % R Cm 2  Aboveground Fine roots Soils Total  195 80 6,575 6,850  3 1 96 100  NPP gCm'yr'  %  65 55  55 45  115  100  In preparing a C budget for a lawn area, the focus should be on the soil as it represents 96% of the C budget. Although lawn does not represent a major contribution to C storage, the soil under lawn represents a major C pool. Lawn can be considered as a use that can be converted to a system in which greater C is assimilated and stored, for example, if it is planted to trees. The NPP value for a lawn can indicate whether the lawn system is being optimally managed. Therefore, it should be measured for this type of study. Maximizing NPP aboveground also results in maximizing NPP belowground, in the roots and soil. Managing for maximization of the NPP results in an increase in the long-term C in storage. Further, the C in composted lawn clippings enters the long-term soil C storage pool. Thus, lawn use may have some direct and indirect ameliorative effects on global warming. 3.3.6.4 Carbon Storage and NPP Budget - Rhododendrons  The budget developed for rhododendrons required the selection of a representative rhododendron. The questions then arose: how should it be selected? What criteria should be used? The approach taken in this study was to select vegetation which is well established - not at a juvenile stage of growth and not at a decadent stage - when growth is steady and before  103  senescence. A review of the C in storage and NPP (Figures 3.2 and 3.3) suggests that C in storage and the NPP increased in the rhododendrons with increasing age up to 20 years. Beyond 20 years, growth began to slow. Thus, the 20-year-old rhododendron is considered to reflect the conditions required. The C budget of rhododendrons prepared is based on the data collected on the 20-year-old rhododendrons (Table 3.27). The 20-year-old shrubs were approximately 2m in height and 1.8m in diameter (Table 3.1). Table 3.27 Carbon budget of 20-year-old rhododendrons.* Pool  Storage 8 Cm"  2  Aboveground 5,957 Coarse roots 856 Small roots 856 Fine roots 1,711 Soils 7,506 Total 16,886 * Interpret budget with caution.  % 35 5 5 10 45 100  NPP g C m" y r 1 ,390 40 421 856 2  1  2,707  % 51 1 16 32 100  The estimated C budget of a 20-year-old shrub in the study area is approximately 16,900 g C m". This should be interpreted with caution. See explanation in Section 3.3.3.2. 2  Approximately 35% of the C is stored aboveground and 20% is stored in the roots. The largest C pool is the soil at 45%. In terms of NPP, the aboveground NPP is approximately equal to the belowground NPP. 3.3.6.5 Annuals  The assessment of C in storage for land used to grow annuals can be focused on the soil pool as annuals are only present during the growing season. The NPP of geraniums, the plant selected as a representative of annuals, was in the order of 82 g C m" yr" in the aboveground portion, and 13 g C m" yr'in the roots. Garden 2  1  2  areas are used to serve aesthetic purposes or for growing food. Therefore, they will likely  104  always occur in the urban setting. The use of land for garden areas does preserve it for a potential future land use options which could include the use of vegetative material which has a greater C storage and assimilation potential than annual flowers and vegetables. 3.3.6.6 Carbon Storage and NPP Budget - Forest, Agricultural (Hay), Urban  A comparison between all land uses indicates that a natural forest is the best land use to maximize C in storage (Table 3.28a). The budget for the natural forest indicates that there are several C storage pools, however, the most important ones are the aboveground portions of the trees, representing 62% of the budget, and the soil representing 20%>. The aboveground understory pool, the fine and small roots of trees, and understory roots represent minor components of the forest C budget, at less than 1% of the total budget. Coarse roots are relatively important at 11%. Litter and coarse woody debris contribute 5% of the C in storage. Therefore, the focus in preparing the C budget for natural forests should be on the aboveground portion of the tree, the coarse roots, and soils. These three pools represent 93%> of the C budget. The urban forest budget is the next land use in which C storage is maximized (Table 3.28a). Similarly, the major C pool is the aboveground portion of the tree. The difference between the natural and urban forest is related to the environment under the tree canopy. The urban forest is defined, for this study, as the tree component of an urban setting. It differs from the natural forest as the litter and coarse woody debris are removed and the soils are more highly managed. Lawn and garden replace the understory of a forested site. An urban forest is more open as the trees are generally sparsely spaced. If the forest in the urban site is not managed, the natural forest C budget can be a proxy for it. Similar to the natural forest, the major C storage pools are in the aboveground portion of the tree, the coarse roots, and the soil.  105  Table 3.28a Summary of carbon budgets - storage. Pool  Natural Forest  Urban Forest gCm" 18,000 (64%) 2  Aboveground  18,000 (62%) Aboveground 115 understory (<1%) Coarse woody debris 615 (2%) Litter 945 (3%) 3,270 3 270 Coarse roots (12%) (11%) 100 100 Small roots (<1%) (<1%) Fine roots 185 185 (<1%) (<1%) Understory roots 115 (<1%) Soils 5,915 6,575 (20%) (23%) Total 29,260 28,130 Interpret rhododendron data with caution.  Hay  Lawn  gCm130 (1%)  Rhododendrons*  gCm" 195 (3%)  gCm 5,955 (35%)  150 (2%)  80 (1%)  855 (5%) 855 (5%) 1,710 (10%)  8,795 (97%) 9,075  6,575 (96%) 6,850  7,505 (45%) 16,885  2  2  2  These components represent 99% of the total C budget. Therefore, data collection for an urban forest should be focused on these pools. It should also include the contribution of other surface treatments that occur at ground level, such as lawn. Woody shrubs, such as rhododendrons, are the next major land use consideration as a means to maximizing C in storage. They are evergreen, thick, woody shrubs with a complete surface area occupied by clumps of large leaves. The aboveground portion represents 35% of the budget with the roots also representing a major C pool. The soil represents the largest C storage pool of the rhododendron budget, higher than in the forest budget. The higher C content of rhododendron soils, compared with the forest soils, may be a response to the larger amount of small andfineroot biomass. The aboveground portions of the shrub plus the soil represent 80% of the total budget. Estimation of the aboveground portion of the shrub as well  106  as the roots was challenging due to the restriction on destructive sampling in the urban area and the paucity of information in the literature on shrubs. The rhododendron data should be interpreted with caution. The major difference between the rhododendron budget and that of the Douglas-fir trees is the relative size of the root pools. The aboveground C of the 20-year-old rhododendrons was estimated at 33% of the Douglas-fir trees but the amount of C in the rhododendron root pool was estimated at 96% of the tree root pool. It is the root biomass that accounts for the high C budget of the rhododendrons. A review of the C budget of hay indicates there are only three components: the stubble, fine roots, and soil (Table 3.28a). The soil is the major C storage pool representing 97% of total C . Therefore, it seems that when developing a C budget for hay crops, focus should be on the soils. Hay crops (and uses such as pasture) likely represent the agricultural use to select for maximizing C, recognizing that the major C pool is the soil. Therefore, other agricultural crops will likely have lower C storage. In addition, the soils under other agricultural crops, other than hay, are cultivated regularly and thus will have a lower C content than the soils under hay. Other local agricultural crops such as vegetables can be equated to the budget prepared for annuals. For this group, the main C pool is the soil. Soils under hay contained 49% more C than the natural forest soils, based on grams of C in the 0-20-cm depth. Because of the higher C content of the soils, the difference between the forest and hay budgets is narrowed. The C budget of hay is substantially less than a natural forest, differing by a factor of three. The lawn C budget is the use in which C storage is the least. It was only 75%> as effective as the hay crop. Similar to the hay crop, the soil was the major C storage pool,  107  representing 96% of the total C budget. More stubble was recorded in the lawn than under hay. This is attributed to the close spacing of turf grasses as compared to hay grasses. Similar to all of the other uses, except rhododendrons, fine root biomass in lawns is not a major C storage pool, representing one percent of the C budget. Fine roots across all uses were a minor component of the total budget, being less that two percent (except for rhododendrons at 10%). Therefore, fine roots are not a major focus in the preparation of C budgets. Hay crops have the highest soil C of all the land uses investigated for this study. This indicates the importance of soils in terms of storing C and that land use has an effect on C storage. This has important implications in terms of global warming. As well, it provides a good argument to preserve soils for crop production and to consider them as a major C storage pool. Soils represent 20% of the total C budget under the natural forests, greater than 95% of the budget under hay and lawn, and 45% of the C pool of land under rhododendrons. These can be considered as trends for forest, agricultural, and urban land uses. The C content of the upper 20 cm of the agricultural soils is 32% greater than the forest soils (Tables 3.20 & 3.21). (The differences in C between the forest and agricultural soils for each depth and for the 0-20-cm composite, are significantly different (p=0.03) [Mann Whitney, two independent sample test]). The C in the agricultural soils is 25% greater than the lawn soils. The higher C content of the soils under hay production is attributed to the occurrence of the fine roots of grasses. The soil C in the agricultural soils is also higher in the 0-10-cm depth compared with the 10-20 cm depth, as expected. Fine root biomass is greater in the upper 10 cm of the soils under hay compared to the forest soils (Figures 3.4a-d). Fine root biomass was less in the agricultural sites than in the forested sites in the 10-20-cm depth, yet the C content of the lower horizon is greater in the agricultural soils. The soil C in the lower depths in the 108  agricultural soils may be a result of the transfer of C to the lower depth through cultivation. As approximately 92% of the fine root biomass occurred in the 0-10-cm layer in the agricultural soils, sampling in the upper 10 cm of the soil under hay production may be sufficient to obtain an estimation of fine root biomass. This is not recommended for forest soils. Approximately 45% of the fine root biomass were located in the 10-20-cm depth.  Figure 3.4a Carbon in roots in agricultural (hay) soils, Abbotsford, B.C.  6000 / 5000  Soils (gCm ) 2  4000 3000  lAgric. soils 0-10 cm  2000  lAgric. soils 10-20 cm  10004 04-  A1  A2  A3  A4  Sites  Figure 3.4b Carbon in agricultural (hay) soils, Abbotsford, B.C.  109  • Forest roots 0-10 cm • Forest roots 10-20 cm F1  F2  F3  Sites  Figure 3.4c Carbon in roots in forest soils, Abbotsford, B.C.  • Forest soils 0-10 cm • Forest soils 10-20 cm F1  F2  F3  Sites  Figure 3.4d Carbon in forest soils, Abbotsford, B.C.  There is more consistency between fine root allocation and soil C in the forest soils. This indicates the close relationship between fine roots and soil C which is masked in the agricultural soils likely because of periodic cultivation. Agricultural soils arefrequentlyviewed as a net source of C O 2 . This occurs as a response to cultivation (Li, 1995; Houghton, 1995). The agricultural sites in this study are well-established hay producing fields. Therefore, they have not been subject to regular cultivation and under such conditions were considered to be at a steady state. The lawn areas can also be considered as long-term grasslands.  11  The difference in vertical distribution of fine roots in the soils under hay production compared with the forest soils may be related to the Db of the agricultural soils. It averaged 0.90 kg l" in the 0-20-cm depth in comparison to the forest soils that averaged 1  0.73 kg l" (Table 3.17). The Db of the agricultural soils was 41% greater than the forested soils 1  in the 0-10-cm depth and 11% greater in the 10-20-cm depth. This is expected as the agricultural sites were subject to vehicular traffic, related to management, with the greatest impact occurring on the surface soils. During management and harvesting, the hay fields are traversed by heavy equipment that results in compaction in the soils. In the forested areas, the soils are not subject to anthropogenic compaction. Therefore, the higher bulk density of the agricultural soils may have impeded root penetration into the lower depths. The difference in root distribution could also be due to a difference in the rooting patterns of grass compared to those occurring under forest cover. The Db of the urban sites was similar to the agricultural soils. This was not anticipated considering that the lawn areas and the urban park area are subject to frequent foot and mower traffic during on-going management practices. The 1.5-cm cores in the urban areas were much more difficult to excavate than the 1.5-cm cores in the agricultural areas. An explanation for the greater difficulty in penetrating the urban soils may be related to a lower soil moisture due to the concentration of finer roots at shallow depths and the presence of thatch in the lawn areas. In the hay field, the soils tended to be bare at the surface between the grass plants. In the urban areas, the grass plants occur close together. Of interest is the consistent pattern of soil C despite the different land uses. The soils are subject to different inputs such as forest litter, coarse woody debris, and plant roots in the forested sites. They are subject to applications of manure, chemical fertilizers, and to different management treatments in the agricultural and urban areas. As well, there are differences in 111  hay and turfgrass roots and rhododendron roots. Despite these differences, approximately 60% of the soil C occurred in the upper 10 cm and 40% in the lower 10 cm across all of the land uses (Tables 3.20, 3.21, & 3.22). This consistency was not expected. It is not explained based on reaching a maximum soil C content as the C content differed between the land uses. The same pattern occurred in soils of high and low C content. This has implications for soil sampling for C content. As approximately 60% of the C occurred in the upper 10 cm of the soil despite the land use, testing of the upper 10cm may be a means for estimating soil C to the 20-cm depth. This may be useful for some sampling programs. Greater testing of this is recommended for soils in other regions. A review of the C budgets indicates that where the crop is not harvested, the major C storage pools occur in the aboveground portion of the plant and in the soils. These are the two largest storage pools. This suggests that if the objective is to maximize C in storage, selection of a land use should be one in which harvesting and cultivation are not carried out. Ideally a natural forest is the option which should be selected to maximize C in storage. Trees, of course, could be harvested and result in the loss, for example, of 18,000 g C m" in storage. However, depending on thefinaluse, removal of the trees may or may not result in a large release of C back into the atmosphere. If the trees are harvested and the slash is burned, the C from the slash will be returned immediately to the atmosphere, potentially contributing to global warming. If the trees are used as fuel wood, almost all of the C except for any charcoal formed, will be released into the atmosphere. Harmon et al. (1990b) analyzed the fate of C of harvested old growth trees. Approximately 18% would be lost from long-term storage as defects and breakage, and 9% would be lost as bark fuel and mulch. This would leave 73% that could be converted to long-term storage. Long-term storage products, as defined by Harmon et al. (1990b), are products that have a residence time of greater thanfiveyears. They include 112  boards and plywood. If the wood is converted to sawdust, scrap, fuel, paper, and residue, it enters the short-term storage pool and will contribute to global warming. In addition, fossil fuels may be used to run the processing operations for making the various wood products, further contributing to global warming. This suggest that harvesting of the trees will result in a net release of C back into the atmosphere, to a greater or lesser extent, depending on the fate of the wood. It is assumed that the roots and the rest of the material remain on-site and therefore are part of the long-term storage pool. The other argument is that if the trees are harvested and 73% is put into long-term storage, a new crop of trees can be grown sequestering more C from the atmosphere. When these are harvested, an additional 73% of the C sequestered from the atmosphere will be transferred into long-term storage. Gradually, more and more C will be sequestered from the atmosphere and enter the long-term C storage pool. That is, 13,000 g C m" sequestered from the atmosphere as C O 2 can be transferred into long-term 2  storage at each rotation (based on a 40-year-old Douglas-fir tree). With an older stand, more C would be transferred into long-term storage but the rotations would be fewer. A summary of the NPP budgets has been prepared (Table 3.28b).  113  Table 3.28b Summary of carbon budgets - NPP. Pool  Natural Forest g C m yr (proportion of total)  Urban Forest g C m yr (proportion of total)  g C m" y r (proportion of total)  g C m" yr" (proportion of total)  1  Rhododendrons * g C m" yr" (proportion of total)  525 (64%) Aboveground 25 understory (3%) 70 Coarse roots (8%) 65 Small roots (8%) 115 Fine roots (14%) Understory roots 25 (3%) 825 Total • Interpret data with caution.  525 (68%)  395 (80%)  65 (55%)  1,390 (51%)  2  Aboveground  1  2  1  Hay  Lawn 2  1  2  70 (9%) 65  2  1  115 (15%)  95 (20%)  55 (45%)  40 (1%) 420 (16%) 855 (32%)  775  490  115  2,705  (8%)  In the forest system, 64% of NPP occurs in the aboveground portion of tree. In the natural forest system, approximately 16%> of the NPP occur in the fine roots. However, because of dieback, C sequestered by the fine roots does not contribute to long-term C storage in the tree. However, C sequestered by thefineroots is likely one of the major contributors to soil C. The amount of C sequestered by the aboveground portion of the hay crop is approximately 75%> of that of the aboveground portion of the tree. The difference between the two is that the hay is harvested and thus not transferred into long-term storage. Hay fine roots sequester 80%> as much C as thefineroots of the Douglas-fir trees. The land in hay production, above- and belowground, sequestered 60% as much C O 2 as trees. Therefore, if the objective is to sequester C O 2 , hay may be considered an alternative if tree or shrub production is not an option. A mixed land use option could also be considered. Twenty-year-old rhododendrons sequester more C annually than all other uses. They sequestered three times as much as the Douglas-fir tree. The rhododendron data should be 114  interpreted with caution. Rhododendron stems and leaves increase in biomass with time. The older leaves increase in biomass and then drop from the shrub by the end of their second growing season. They were included in the NPP budget as there is a new on-going supply of old leaves, as the new leaves become old leaves in their second growing season. The current year's stems, leaves, and buds represented 44% of the aboveground NPP (Table 3.11). Lawns sequester the least amount of C annually. However, the range in production indicates that the amount of C sequestered in the aboveground biomass and in the roots can be increased substantially with the use of fertilizers although chemical fertilizer synthesis contributes to greenhouse gas production. An increase in production in lawns can result in an increase in the quantity of grass stubble, which represents the long-term C storage. As well, it can result in an increase infineroot biomass that contributes to the soil C pool. The NPP of hay is in the order of four times the amount of lawn. The lower amount of lawn production may be a negative response primarily to cutting frequency. Roots also respond negatively to cutting frequency. The NPP offineroots of hay was almost double of that of the lawn. 3.4  SUMMARY AND CONCLUSIONS  The C budgets for forested, agricultural (hay), and urban (lawn, trees, shrubs, annuals) land uses were carried out in the same region and on the same soils to reduce the influence of non-land use effects including climate, allowing for meaningful comparisons to be made among the land uses. As well, the samefieldand laboratory protocols were used across all land uses to reduce systematic error. The C budgets indicate that second growth Douglas-fir trees under natural conditions have the greatest amount of C of the three land uses. They store three times as much C as a hay  115  crop, four times as much as lawn, and 40% more C than rhododendrons. Annual gardens have low C budgets. Soil is the only major C storage pool of annuals/vegetable gardens. The comparison of the budgets of the three land uses indicates that soil is a major C storage pool. Soil represents 20% of the forest C budget, 45% of the rhododendron budget, and greater than 95% of the hay and lawn budgets. The difference between the forest and hay budgets, being large from the immense storage pool occurring in the trees, is lessened due to the greater amount of C stored in soils under hay, In the natural forested sites, the aboveground portion of the trees represents 62% of the total pool. Therefore, a land use with non-harvested, woody vegetation, such as forest, maximizes C storage. If this cannot be applied over large areas, it can be included as pockets within other uses. Understory in the natural forested sites is a minor component of the C budget. It represents less than 1% of the total budget. Roots generally are not major C storage pools except for tree coarse roots. Coarse roots are not subject to dieback. Fine roots across most uses, except rhododendrons, represented about one per cent of the total C storage budget. This suggests that under limited resources for field studies less focus can be put on this compartment. Rhododendrons represent a major C pool, being second to trees. Thus, they could be considered as an alternative to trees. They are woody and grow to substantial sizes, storing more C with time. As well, being evergreen they retain the C from new growth. This suggests that other woody, evergreen shrubs are also important in terms of C storage and assimilation rates. Therefore, to maximize C storage and assimilation rates in urban settings, planting of trees is the best choice. If this is not an option, woody, evergreen shrubs allowed to grow over  116  an extended period of time are another means of promoting C storage. Planting of shrubs maintains the soil C storage pool. The difficulty in preparing a C budget for rhododendrons in an urban environment is that only light clipping can be carried out and whole harvesting is usually not feasible. As well, there is a paucity of information on the various components of the shrubs. More research is required in this area. More destructive sampling is required to confirm the relationships between the various components of the shrubs. This will provide a means to estimate biomass from a few parameters as has been developed for trees, i.e., through the development of allometric equations. The results of the study indicate that, across all land uses, the partitioning of soil C was similar. That is, approximately 60% of the soil C occurred in the upper 10 cm of the soil and 40%) in the lower 10 cm of the soil. More research should be carried out to confirm if this occurs across regions. This may have sampling implications in studies of soil C and may be useful if large areas are being mapped. It is important that site specific sampling of the soils be carried out. Although the C partitioning with depth was similar in all land uses, C in soils was affected greatly by land use. For example, the soil under hay contained 49%> more C that the natural forest soils. The soil C of the hay crops is the highest of all the land uses investigated in this study. In terms of fine root partitioning, 90%> of the fine roots occurred in the upper 10 cm of the soil under hay and 85% in the lawn areas. In the forested sites, the partitioning of roots followed the same trend as the partitioning of the C in the soils. The difference between the fine root depth patterns of hay and lawn, compared with that of the forested areas, is attributed to the differences in Db of the soils and the nature of the roots. That is, grass roots are predominantly fine. Roots from trees and understory occur at irregular spacing and vary in 117  size. The Db of the agricultural and urban soils is higher than found in the forest soils, particularly in the upper 10 cm of the soil. Forest litter was collected twice. Once in late summer, prior to leaf drop but following a summer in which the previous year's litter had undergone decomposition, and secondly in the spring, after leaf drop but prior to spring-summer decomposition. The litter was collected at these times to assess the effect of sampling periods on litter biomass. There was 50% less litter in the fall than in the spring on sites with deciduous trees. The large decrease is attributed to the deciduous component of the litter. Deciduous litter has a higher decomposition rate than coniferous litter. There was little biomass difference between the fall and spring litter at the coniferous site. This has implications in choosing the most appropriate sampling period. That is, the sampling time should be governed by the objective of the research and not on ease of collection. If the sampling period is restricted, the researcher should recognize the implication that the sampling period may have on the results. In the coniferous forests in the study area, litter sampling for carbon quantity can be carried out any time during the year. Averaged for all sites, litter represented 3% of the forest C budget. The lawn and hay portions of the study indicate that C storage and assimilation can be increased through management. Therefore, it is not only important to select the appropriate vegetative cover but also to manage it for maximum C assimilation and storage. A hay crop sequesters, during the growing season, 75% as much C aboveground as a Douglas-fir tree. A difference between a tree and hay is that the hay is harvested. Therefore, the assimilated C is not transferred into long-term storage. This has implications in terms of the goal of a C sequestration program. Hay can be considered a short-term sink. If hay production is maximized, for example, through the application of natural fertilizers such as manure, then the amount of C stored under hay use can be increased. Once added to the soils, 118  the C from manure contributes to the soil sink and therefore enters long-term storage. A portion of the C assimilated by the root biomass is also transferred into the long-term soil C pool. Although these findings are based on the site characteristics of Abbotsford, B.C., the trends should be similar to other temperate regions. The compartmentalization of the C budget is a guide for the researcher. It can be used to address the major and minor storage pools. It can be useful in illustrating where the research should be focused and to indicate deficiencies in data that can have a major impact on the results of the C budgeting. The focus on the important pools can permit a good estimation of C budgets most efficiently. This would facilitate comparisons between different regions. The implications of the above C budget assessments must be taken in perspective. As land is urbanized, portions are covered with impermeable surfaces. Therefore, the amount of land available in an urban environment for C sequestration and transfer into long-term storage in the vegetation and the soils is controlled by the amount of such surfaces. Ideally, if the goal is to maximize C sequestration, then planning decisions at all levels, from the individual stakeholder through to the regional managers, should be to minimize the amount of land covered with impermeable surfaces. Once that decision is made, one may select the vegetative cover in consideration of its potential to assimilate and store C. In Abbotsford, B.C., the land use to select to maximize C storage is forest. Where this is not an option, inclusion of trees within pockets of other uses could be considered. The results of the comparison indicate the choice of land use can have a major impact on C storage and assimilation rates. That is, land use can play an important role in global warming, in maximizing the sequestration of C O 2 from the atmosphere and in insuring that it is  119  maintained in storage. This can be done at the residential lot, municipal, regional, and higher levels.  120  4  U S E O F T H E C E N T U R Y M O D E L T O A S S E S S T H E IMPACT O F L A N D U S E O N C A R B O N S T O R A G E A N D ASSIMILATION  4.1  RATES  INTRODUCTION  This chapter outlines the use of the CENTURY model to produce C budgets for the forested, agricultural, and urban land use sites in Abbotsford, B.C. The chapter is set up so that the inputs used to produce the model simulations are presented in the first part and the results of the simulations are discussed in the second part. The output is produced as a result of interactions among the inputs (described in Appendix A). Questions arose during the preparation of the model simulations: To what extent do some inputs affect specific output? Which inputs have little or no effect on the output in the range reflective of the study area? What is the level of precision required? These questions were answered through a series of sensitivity analyses. Such tests of sensitivity indicate the preciseness required in data collection to obtain sufficient confidence in the model results. These tests were also designed to indicate the amount of variation in output associated with the input or prediction error (Kimmins et al, 1990). Thus, they provide a measure of the confidence of the outputs based on the inputs. These sensitivity analyses are discussed in the first part of this chapter. The CENTURY model allows for a range of management options, scheduling of events, and lengths of simulations. These must be selected and taken into account for proper data interpretation. There are other benefits of using the CENTURY model for producing these C budgets. The model simulates additional parameters not measured in thefieldstudy. For example, it  121  simulates the microbial populations, the decomposition respiration which is used in the calculation of the net ecosystem production (NEP), and the active, slow, and passive soil C pools. The NEP can be defined as the NPP minus the decomposition respiration. (NEP is the difference between the gross primary production [GPP] and the total respiration by an ecosystem (autotrophic [R ] and heterotrophic [Rh]) (Waring and Schlesinger, 1994). It a  represents the net amount of C assimilated by a system and is used to establish if a system is a net C source or sink (Hutjes, 1998). Direct measurements of NEP can be made using eddy covariance flux techniques (Hutjes, 1998). Not only can the changes in these pools be tracked over time but the effects of management on these parameters in the short term can be assessed. These extra parameters further the understanding of the C dynamics under these land uses. Even though the focus of this study was to assess the land use effects on C storage and assimilation, the value of using a computer model such as CENTURY is to indicate long-term trends, whether the various land uses are C sources or sinks, as this has implications in terms of global warming. As the simulations of the CENTURY model produce trends in C flows over time, these provide a means to evaluating the sink/source potential of a land use. Therefore, the objectives investigated in this chapter are: 1) to evaluate the sensitivity of the model to the various input parameters; 2) to produce the simulations for each type of land use; 3) to assess the land use to maximize C storage; 4) to assess the suitability of the model for comparing the effects of forested, agricultural, and urban land uses on C storage and assimilation; and 5) to assess if the land uses are C sources or sinks.  122  4.2 SELECTION OF THE DEFA UL T DA TA SETS  The default data sets are located in a tree/crop file and corresponding sitefile.These files are directly related. The top of the sitefilemakes reference to the corresponding crop/tree file. The Douglas-fir simulations relied on the default data set of the H. J. Andrews Experimental Forest in Oregon, U.S.A. (latitude 44° N, longitude 122° W). The H.J. Andrews site is located in a similar biophysical region as the study area, with a cool, temperate climate. This forest is used for intense research. The results of the studies carried out at this centre are well documented. The sitefilefor the forest simulations was based on the Tconiffile,which is also based on the H.J. Andrews site. The hay crop was based on the default data set of a grass crop and the corresponding C3 grass site file. The data originates from a research centre in Colorado, U.S.A. (latitude 40° 9', longitude 104° 8'). This sitefilewas selected, as it was the only default data set in CENTURY based on C3 grasses. Site specific data were entered to adjust it to the study site conditions. The same defaultfileswere used for the lawn. 4.3  SIMULATION LENGTH  The simulations were run for a duration of 20 years. For the forest simulations, the starting point was based on a second growth Douglas-fir forest of approximately 40-years-old and ending with the forest of approximately 60-years-old, reflecting the age of the trees in the study area. The goal was to produce a simulation over a period that would reflect the forested sites in terms of age and sufficient to illustrate the effects of hay and lawn production.  123  4.4  MODEL INPUTS AND SENSITIVITY ANALYSES  A generalized scheme of the model was adapted for this study (Figure 4.1). The sitespecific inputs are divided into three main categories: location inputs, vegetation inputs, and soil inputs. The location inputs are discussed first, followed by the aboveground inputs of the forested systems (both natural and urban), the hay system, and then the lawn system. The soil and root inputs for all three systems are discussed in the same sections as they are common to all three systems. The sensitivity analyses were carried out by substituting input values of the Douglas-fir routine with other input values and comparing the NPP and C in storage outputs. As the NPP is frequently used to test the model, it is the focus of the sensitivity analyses. Sensitivity analyses were also carried out for the hay crop using varying NPP values. The following is a description of the input data for each of the land uses and the management programs designed for the simulations. The management scenarios are designed based on those typical of the study area. A list of the input data for the simulations and the results of the sensitivity analyses are included in Appendix C. 4.4.1.1 Latitude and Longitude  The latitude and longitude for the Abbotsford area were input. Latitude is used in the potential evapotranspiration calculation produced by the model.  124  INPUTS  LOCATION  SOILS  TEXTURE  NPP  BULK DENSITY  FOREST RESPIRATION  CLIMATE LATITUDE & LONGITUDE  VEGETATION  DEPTH ORGANIC MATTER  FOREST C:N FOREST C STORAGE LAI CWD  (FOREST)  NPP ABOVEGROUND NPP BELOWGROUND C STORAGE -ABOVEGROUND C STORAGE - BELOWGROUND SOIL CARBON  NPP: Net Primary Productivity; LAI: Leaf area index; CWD: Coarse woody debris  Figure 4.1 CENTURY model flow chart.  125  4.4.1.2 Climate  Climate data from 1972-1992 were input. These included monthly maximum and minimum temperatures and precipitation. The CENTURY model has the capacity to generate precipitation means, standard deviations, skewness values, and maximum and minimum temperature means from the data (Metherell et al, 1993). The model allows the user to run the simulatios with the actual data or the data to which CENTURY has applied statistics. The latter is useful to simulate runs for longer periods of time than the period that the record covers. In this study, the actual data records were used. 4.4.1.3 Natural and Urban Forests  The literature was critically reviewed in Chapter 3 to obtain values for some pools in the forest C budget. However, there are several additional input variables associated with the forest budgets required by the model. For example, the inputs for trees include the maximum monthly above-and belowground NPP, gross productivity, C and nitrogen in storage, and C:N ratios of the various components, including the foliage, branches, stem, and coarse, small, and fine roots. The literature was re-examined to determine if the input values for these parameters from the H J. Andrews default data set reflected the range of second-growth Douglas-fir occurring in the study area. The studies by Cole et al. (1967) on Douglas-fir trees in the Charles Lathrop Pack Experimental Forest near Seattle, Washington, and that of Keyes and Grier (1981) on Douglasfir forests at the Cedar River Research Station in Western Washington could supply this kind of component data. The sites are similar in terms of biogeoclimatic conditions to those of the study area. As well, the Douglas-fir forests are similar in age to those in the study area.  126  The following is a description of each of the aboveground forest inputs. The differences between the natural and urban forest simulations are also discussed. 4.4.1.3.1 N P P For the NPP input, the potential maximum amount (above- and belowground) is required such that the model will adjust downward as a result of site and climatic limitations. If the sites are not limited by the site and climate factors, then the production will be at maximum. The input value for NPP has been based on the high productivity 40-year-old Douglasfir stand investigated by Keyes and Grier (1981). This value is used indirectly. This is because the model requires that the NPP input for trees be entered as the potential maximum monthly productivity. A value for potential maximum monthly production is not readily available in the literature. Monthly data are difficult to estimate over such a short time step. Biophysical factors such as temperature (Waring and Schlesinger, 1994), light (van den Driessche, 1987), and the moisture available to the trees (Grieu et al, 1988), all of which vary throughout the growing season, affect the NPP. In the early spring in our study area, there is generally sufficient soil moisture for growth, however, the temperatures are relatively low and the days are short. With the progression of the growing season, the days become longer, the air temperature increases to optimal levels for photosynthesis, but the soils begin to dry out. The trees then can be subject to moisture stress and thus photosynthesis is slowed. Waring and Schlesinger (1994) note that the optimum temperature range for photosynthesis varies with species, but it is commonly between 15° C and 25° C for temperate trees. In the study area, these temperatures occur between May and September. The Abbotsford area has a climatic moisture deficit (BCMOE, 1978) and this likely occurs in late July, early August, the months of lowest precipitation and highest temperatures. As well, high temperatures also negatively  127  affect photosynthesis. Maximum temperature extremes have been recorded in July at 3 7 . 8 ° C, and in August at 3 6 . 3 ° C (Environment Canada, 1980). It would seem then, that in the study area, the maximum monthly production occurs in late June-early July when the temperatures are warm, the days are long, and there is still sufficient moisture in the soil. The question then is: how much of the annual NPP occurs in that optimum period in the study area? As no information is available, a value of 25% was selected based on conversation with Dr. Andrew Black, U.B.C. This was based on the assumption that the NPP is not equally divided among the growing season months in the study area due to differences in temperature, length of daylight period, and soil moisture. The following is the pattern of NPP estimated for the study area from which the monthly maximum production of 25% was based:  April 5%  August 20%  May 15%o  September 10%  June 25%  October 5%  July 20%  Based on this estimate, 90%> of the NPP would occur between the traditional growing season for the study area, that is, between May and September. The sites in the study area are estimated to be of medium site productivity, in the range of the average of that of the sites studied by Keyes and Grier (1981). Based on this, the forest simulations, both the natural Douglas-fir and urban forest, were run with the maximum estimated monthly NPP (225 g C m" per month), based on the annual production of the high 2  productivity site in the study by Keyes and Grier (1981). It was expected that the annual  128  productivity generated would be similar to the average annual productivity reported by Keyes and Grier (1981), being reduced from the high productivity input by site limitations in our study area. The expected N P P was approximately 825 g C m" yr", for the above- and 2  1  belowground components. To test if the maximum monthly percentage selected was reasonable, sensitivity analyses were carried out. As part of the sensitivity analyses, different maximum monthly production percentages were selected. Using the maximum monthly N P P input of 225 g C m", the model generated an annual 2  N P P of 868 g C m" yr". This value of 868 g C m" yr" is 5 % above the expected average 2  1  2  1  annual N P P of 825 g C m" yr" and 9 6 % of the high productivity site (860 g C m" yr") 2  1  2  1  researched by Keyes and Grier (1981). This value was obtained by basing the maximum monthly amount on 2 5 % of the annual N P P of the high productivity site (Keyes and Grier, 1981) and running the simulation with site-specific input from the Abbotsford area. That is, the the amount of 868 g C m" yr" was within 5 % of the expected N P P or 43 g C m" yr". 2  1  2  1  Basing the maximum monthly N P P on 1 6 % of the annual productivity, the average annual N P P generated was 7 5 % of the annual N P P generated in the base simulation (Appendix C ) and 79%) of the expected N P P (825 g C m" yr"). Using a monthly estimate of 3 3 % of the 2  1  annual N P P , the average annual N P P generated was 3% greater than the base simulation and 8%> higher than the expected N P P . That is, there was only 3% difference in the N P P generated between using a value of 2 5 % and 33% of annual N P P to represent a maximum monthly amount of N P P . The monthly percentage N P P of 2 5 % base value lies halfway between the 16%) and the 33%, and yet a large discrepancy of 34% in generated N P P occurred between the 16%o and 25%o range, with only a 3% difference between 2 5 % and 33%.  129  The wide discrepancy resulting from using 16%, which did not occur using 33%>, indicates that the model was more sensitive at the lower range, likely because the site conditions using 16%> were not consistent with the NPP that the study area can produce. This suggests that the value of 25% more closely resembles the maximum monthly production occurring in the study area, reflecting local climate and other site conditions. This trend was also evident in terms of stored C in the tree foliage, litter, soil litter, and fine roots (Appendix C), further suggesting that a maximum monthly NPP being 25%> of the annual NPP, is a reasonable estimation. The skewness of the data at the lower percentage, and not occurring at the higher percentages, indicates the importance of performing sensitivity analyses. As the output is affected by the input, the results should be interpreted in a relative capacity rather than as absolute value. Therefore, a sensitivity analysis was performed to estimate the sensitivity of the model to the NPP input value. The question was asked: What is the impact of basing the simulations on the high productivity site, the low productivity site, or an average? When considering the above- and belowground components of the study by Keyes and Grier (1981), the monthly NPP of the high productivity site was only 15% (or 15 g C m") greater than the NPP of the low site in that study. The results of the sensitivity 2  analysis indicated that using the low input value resulted in 4%> less annual total NPP compared with the high productivity site. There was only a 1%> difference in the above- and belowground C in storage and in the soils, and a difference of 1% for the total C budget. The difference between the average and the high value was 2%> in terms of NPP and 1%> for total C in storage. Therefore, the model does not appear to be sensitive at this range of input values. Thus, the NPP input value was based on the high productivity site described by Keyes and Grier (1981).  130  The aboveground NPP output of the base simulation averaged approximately 2  1  550 g C m" yr" over the 20 years of the simulation (Appendix C). The average between the high and low site reported by Keyes and Grier (1981) was 525 g C m" yr". That is, the model 2  1  simulated within 25 g C m" yr" of the expected aboveground value, indicating good agreement 2  1  with the data. The simulated annual NPP also falls within the range of the aboveground NPP previously reported by other researchers. Turner and Long (1975) reported aboveground NPP of Douglas-fir stands between 42 and 73 years of age, at 465 g C m" yr". Kurz (1989) 2  1  reported aboveground NPP between 235 and 800 g C m" yr" on Douglas-fir stands ranging 2  1  between 32- and 70-years-old, averaging 520 g C m" yr". 2  1  Input is also required on the partitioning of the NPP into foliage, branches, wood, and coarse and fine root components. The partitioning was based on the findings of Keyes and Grier (1981), for an average of the high and low site. The values for the separate components are presented in Appendix C. 4.4.1.3.2 Gross Primary Production Gross primary production (GPP) is the total production whereas NPP represents GPP minus the amount of biomass lost as a result of autotrophic respiration. The model calculates the NPP from the GPP. Soil moisture, soil temperature, leaf area index (LAI), wood nitrogen content, and the amount of sapwood, affects GPP. The model assumes that only the sapwood part of the tree respires. Therefore, a value for the maximum monthly GPP for trees was required to carry out a simulation. A literature search was conducted to determine the respiration rate for entry into the model. Information on the respiration rates of trees is considered to be incomplete and inadequate (Gower et al, 1995; Landsberg, 1986), and difficult to measure (Benecke and Nordmeyer, 1982). Respiration is greatly affected by  131  temperature (Ryan and Waring, 1992). A general respiration rate commonly used is 50% of GPP (Ryan, 1991). Others have found respiration rates ranging from 25%> to 57%> (Benecke and Nordmeyer, 1982; Linder and Axelesson, 1982). A respiration value of 40% of GPP was selected. Based on this respiration rate, the GPP was set at 623g biomass m" per month, for 2  both the natural and urban forests. A sensitivity analysis indicated that simulations which compared the output using a respiration rate of 40% to that of 30%> and 50% made a difference of 2% or less of the NPP value and total C in storage (Appendix C). There was only a 3% difference in NPP between a simulation in which the respiration rate was 30% compared to one in which the respiration rate was set at 50%>. If the respiration rate is 30% or 50%, this will result in an error of 2% or less in NPP, and 1 % > or less in total C in storage, aboveground over 20 years, compared to the value of 40%. This suggests that the model is not sensitive to respiration rate in the ranges examined and at the lowest potential GPP value input, the NPP is near maximum for the conditions entered. This may not occur under other site conditions. This sensitivity analyses should be carried out to insure a sufficient maximum potential GPP input value such that the GPP value does not artificially limit the NPP. Based on these analyses, a rate of 40% is considered to be a reasonable value. Although the differences between the output were minimal between the high and low respiration rates, the trends indicate that the higher the respiration rate entered into the model the greater the NPP output and C in the biomass and soil. 4.4.1.3.3  Carbon and Nitrogen in Storage  The site-specific inputs include the C and nitrogen (N) allocated to each portion of the tree. The proportions of each component and the C in storage for each component sets the age  132  of the tree. The data were based on the average between the low and high productivity of 40year-old Douglas-fir sites described by Keyes and Grier (1981). The N content of each component was based on the carbomnitrogen (C:N) ratios developed by Cole et al. (1967). These values are listed in Appendix C. 4.4.1.3.4 Leaf Area Index Foliage area is expressed as the LAI. The LAI is a measure of the surface of the leaves projected downward to a unit area of ground beneath the canopy. It can be expressed as twosided, one-sided, and all-sided (for example, for needles). The LAI is an input parameter required by the CENTURY model for the forest simulation. Leaf area is considered essential for estimating photosynthesis (Gholz et al, 1976). Therefore, this parameter is very important. However, similar to the NPP and C in storage of whole tree components, it is not easy to measure. It is measured by a variety of methods and is affected by a number of factors including stand age and climatic conditions. It has been measured by a combination of regression equations (for foliage biomass), and destructive sampling (Gholz, 1982; Gholz et al, 1976), from stem diameter measurements (Chen and Black, 1991; Waring et al, 1978), by branch sampling (Tan et al, 1978), and hemispherical photography (Chen et al, 1991). It is well documented that LAI increases rapidly in early succession and plateaus as the stand matures which may occur as early as in a 40- to-5 0-year-old Douglas-fir stand (Turner and Long, 1975). This is explained by the Pipe Model Theory, which is based on the theory that the unit weight of foliage requires a specific cross-sectional area of conducting sapwood in the crown (Waring et al, 1982). Chen et al. (1991) found that the effect of scattering of visible radiation, exposure time using photography, and clumping in the stand affect the LAI measurement. Chen and Black  133  (1991) found that branch architecture also affects the LAI. In terms of climatic influences, Waring et al. (1978) measured the LAI on mature Douglas-fir stands in the western Cascades of Oregon and found differences in LAI related to differences in moisture stress and temperature with indices (all surfaces) ranging from 23.5 to 51.5. Gholz (1982) also found LAI is strongly related to growing season water balance and temperature. He studied mature Douglas-fir forests in the western Cascades and in the western Coast Range and found LAI (total surface) ranging from 15 to 22 in the low- and mid-elevation areas, with a LAI as high as 47 in the wetter, western Coast Range. Young and thinned stands will logically have a lower LAI. Price and Black (1991) reported the LAI on a 20-year-old, droughty Douglas-fir stand to be 0.2, and a LAI on a 27year-old Douglas-fir stand, also on a droughty site with a higher stocking level, to be 3.5. Gholz et al. (1976) reported a very large LAI of 42.1 (all surfaces) in a stand in the western Oregon Cascades dominated by old growth (250- to 450-year-old) Douglas-fir trees. Because so many factors affect LAIs, the challenge was tofindstudies carried out on Douglas-fir stands which were similar in age to those in the study area and which were carried out in a similar biogeoclimatic zone. As noted previously, the H.J. Andrews research forest has similar biogeoclimatic conditions as the study area, however, the data set is based on old growth Douglas-fir. The LAI for the H.J. Andrews site is 20. Chen and Black (1991) carried out a study on a 26-year-old, unthinned and unpruned Douglas-fir forest stand on Vancouver Island, B.C. which has similar climatic conditions to that of the study area and measured the LAI of the stand as 7.8 (one-sided). Chen et al. (1991) measured a LAI of 9.8 (one-sided) on an 80year-old Douglas-fir stand in Vancouver, B.C. The LAI input value required for the model (Metherell et al, 1993) is defined as the theoretical maximum leaf area index achieved in a  134  mature forest. Therefore, a maximum theoretical leaf area index was selected at 16, which is between that of a young and an old-growth Douglas-fir forest. This value was used for simulations, both of the natural and urban Douglas-fir. A sensitivity analysis was run with LAI's of 8 and 20 (Appendix C). A LAI of 8 resulted in a decrease in total NPP of 2%, in comparison with simulations with a LAI of 16. There was no difference in C in storage in the biomass or in the soil. A LAI of 20 resulted in no difference in NPP or C in storage. Therefore, it appears that the model is not sensitive to the LAI input in the ranges typical of second growth Douglas-fir trees found in this region. The model is designed such that the ratio of aboveground production to LAI is highest at a LAI of 6 and above this value, the LAI does not result in an increase in production rate (Figure 4.2). This graph is based on a 36-year-old Douglas-fir forest in Oregon, U.S., on wellwatered, nutrient rich soils (Appendix C, CENTURY references, Waring and Schlesinger, 1982). The results of the study by Waring et al. (1981) suggest that this relationship may not be applicable to all stands. For example, stands with high stocking levels have greater LAI but lower amounts of harvestable wood due to greater mortality caused by increase competition. As well, the relationship between LAI and tree production is not the same for all tree species or site conditions (Waring and Schlesinger, 1994). Thus, sensitivity analyses for LAI should be carried out for different tree species.  135  0  V. 0  l  1  1  -J  1  2 3 4 5 T R E E LAI ( L E A F A R E A INDEX)  Figure 4.2 Relationship between leaf area index and tree production (Metherell et al., 1993). 4.4.1.3.5 Coarse Woody Debris  The coarse woody debris (CWD) input was adjusted downward from the default data set, as the H.J. Andrews site is based on an old growth forest. The value used for the simulations is based on the findings of the review carried out in the initial preparation of the C budgets (Chapter 3). For the urban Douglas-fir simulations, no input was included for the branch and wood components of the CWD, as it was assumed these would be removed from residential lots. The CWD-coarse root component was reduced to a minimal amount as the assumption was made that owners will also remove the roots, in most instances, when trees are removed from a residential lot, to achieve a uniform lawn area. The CENTURY forest submodel automatically generates coarse woody debris with time as it simulates natural forest conditions. This is achieved by setting a monthly death rate for each component of the tree. The model also provides for input on the decomposition rates  136  of the each component that affects nutrient cycling, soil C and N. The decomposition rates of the branch and wood CWD materials were set to zero in the urban simulation to eliminate the effect of decomposition of the branch and wood CWD automatically generated by the model but considered removed. 4.4.1.3.6 Litter The natural forest litter input of 965 g C m" was based on the average of the findings of the field program. Urban forest litter was set at the minimum as it is generally removed with regular maintenance. 4.4.1.4 Hay 4.4.1.4.1 NPP The model requires maximum monthly potential production of the aboveground portion of the crop input as grams of biomass per metre square. This parameter should reflect aboveground crop production under optimal summer conditions (Metherell et al, 1993). Metherell et al. (1993) note that this parameter will frequently be used to calibrate the model for different environments, species and varieties. Based on the field program, the maximum monthly production parameter was set to 520 g biomass m" in the model. This was based on an estimated maximum monthly 2  production from one of the sites (Site A4). This site had the highest aboveground production and was the most intensively managed of the four farms. Sensitivity analyses were run with 2  2  2  input values at 420 g biomass m" and 620 g biomass m" (Appendix C). At 420 g biomass m" , total NPP output was 2% less than the output obtained with the base input, and 1% greater  137  using an input value of 620 g biomass m" . This indicates that the model is not sensitive to the 2  maximum monthly NPP input parameter for hay in this range. Variation in input value of 100 g biomass per m" from the projected input resulted in an output difference of less that 2% or 5 g C m" yr" , suggesting that the input selected at 520 g biomass m' is a suitable value for 2  1  2  the hay simulation. 4.4.1.4.2 Rotation The hay simulation was based on an eight-year rotation (Table 4.1). The eight-year rotation was used as an example and could have been increased. Table 4.1 Hay rotation. Year 1 2-8  Action • • • • •  April: cultivate May: plant, fertilize August: harvest June: harvest, apply manure August: harvest  The C E N T U R Y model allows for a number of amendments including various chemical fertilizers and organic amendments (Tables 5.2d-e). The application rates of the chemical fertilizers can be selected. The chemical fertilizer selected for use at reseeding was designed to achieve 90% of maximum production. The organic amendment options in the model include wheat/straw and straw/manure. The straw/manure (at 100 g C m" ) was used in the simulation. 2  This was selected as straw/manure is representative of the organic amendment applied in the study area.  138  4.4.1.5 Lawn 4.4.1.5.1 NPP The maximum monthly NPP input value was 84.6 g biomass m", based on the highest 2  monthly production value obtained for all lots, from Site U3-plot. (This represented one sixth of the amount inputted for the hay simulation.) Site U3-plot was intensively managed and was the most productive. The maximum monthly production represented 25% of the annual NPP. 4.4.1.5.2 Litter  Lawn litter was set at the minimum as litter is generally removed with regular maintenance. 4.4.1.5.3 Rotation For the simulation, the lawn was cut on a monthly basis, as CENTURY operates on a monthly time-step. The lawns were fertilized every June at a rate to achieve 75% of maximum production. This is considered reflective of the overall practices in the study area. Some urban sites were very intensively managed and some received very little management other than mowing. The lawn simulation was initiated using an established lawn cover. 4.4.1.6  Soils  4.4.1.6.1 Soil Bulk Density -l  Based on the field program, the average bulk density (Db) was set at 0.74 kg 1 for the forested sites, 0.89 kg l" for the agricultural sites, and 0.90 kg l" for the lawn and urban forest 1  1  sites. 139  Sensitivity analyses were carried out on the Db parameter. These were carried out on the natural forest simulation by substituting the forest Db value, 0.74k g l" , with a range of Db: 1  0.89 kg!" and 1.02 kg l" . There was less than 2% difference among the values in the NPP and 1  1  in the total C budget (Appendix C). This indicates that the model is not sensitive to Db values in the range considered. (It is important to note that Db measurements are required to convert percent C to grams of C in the soil, required for the model simulations). A review of the sensitivity results (Appendix C) indicates that although the difference in between the lowest Db and the highest was 2%, the trends are of interest. The high Db value resulted in a decrease in NPP and in C in the biomass and soils and an increase in the CWD and litter. The Db measurement is used to compute the wilting point and field capacity. The higher Db results in an increase in the death rate (Appendix A, Figure A.l 1) and thus, an increase in the products resulting from death, i.e., CWD and litter. As soils with higher Db have a lower porosity, the available water holding capacity (AWHC) is reduced resulting in a decrease in production and thus, C in storage. 4.4.1.6.2 Soil Carbon The soil submodel has been parameterized to simulate soil organic matter dynamics in the top 20 cm of the soil (Metherell et al, 1993). Soil C inputs were 5,917 g C m" for the 2  2  2  forest simulation, 8,795 g C m" for the agricultural simulation, and 6,575 g C m" for the lawn and urban forest simulations. The sensitivity analyses were carried out on soil C. They confirmed that soil C is a major driving variable of the model. To test this, the forest soil C content was substituted with the values from the agricultural and urban soils. The C content of the agricultural soils was 50% higher than the forest soils. Substituting the forest soil C with the soil C for the 140  agricultural soils resulted in a 14% increase in total NPP. The total C storage budget including the C in the soils, increased by 9% and resulted in a 10% to 13%> increase in C storage in the tree foliage, surface litter,fineroots, soil litter, and in soil respiration (Appendix C). Small increases occurred in the other parameters. This indicates that these parameters, for example, the foliage andfineroots, are affected by soil C as the soil C is a driving variable, linked with the N, P, and S submodels. Soil C increased, for example, due to the increased amount of substrate available for decomposition, and with increased root production as roots contribute to soil C through dieback and thus availability of substrate for decomposition. The urban soils had approximately 11% more C than the forest soils. Whereas a 50% increase in soil C using the C value of the agricultural soils resulted in a 15% increase NPP, the 11% increase in soil C using the urban soils resulted in a 5% increase in NPP. That is, approximately every 10%> increase in soil C resulted in a 5% increase in NPP. Similar to the simulations run with the agricultural soil C values, the amount of C in storage was increased proportionately with the soil C. Therefore, it is very important that the soil C is measured in studies such as this. The CENTURY model requires that values for soil C in active, slow, and passive pools be initialized. There is also a surface microbial pool. The input data for the microbial pool was based on the default data sets except for the urban lawn and urban forest simulations. The microbial pool input for these simulations was lowered due to the impact of using chemical fertilizers in the urban simulation and the decreases in other substrates available for decomposition (CWD and litter) (Appendix C). The active pool represents soil microbes and microbial products and has a turnover time of months to a few years (Metherell et al, 1993). The slow pool includes resistant plant  141  material derived from the structural pool, and soil-stabilized microbial products derived from the active and surface microbe pools. This pool has a turnover time of 20 to 50 years. The passive pool includes the organic matter that is very resistant to decomposition, having been physically and chemically stabilized. This pool has a turnover time of between 400 and 2,000 years. Total C was separated into these pools according to the percentages described by Metherell et al. (1993), namely 3% for the active pool, 62% for the slow pool, and 35% for the passive pool. 4.4.1.6.3 Soil Texture  The soils in the study area are developed on medium textured (silt loam) aeolian deposits. The model requires that the soil texture be initialized on a percent sand, silt, and clay basis. There is a range of the various sized fractions of soil in a textural group. Thus, a medium range was used (31% sand, 62%> silt, and 7%> clay) in the simulations. Sensitivity analyses were carried out to estimate the impact of soil texture on output. The finer and coarse ends of the silt loam textured range were compared to the medium grouping. Thefinergrouping (e.g. 20% sand, 70% silt, and 10%> clay) resulted in a decrease in NPP of 6%, a decrease of 5%> in the amount of C in the leaves, but less than 1% in the other tree components (Appendix C). It also resulted in a 4%> decrease in litter, however, it had no effect on the coarse woody debris. This suggests that the decrease in litter was related to a decrease in leaf biomass. Fine root biomass was also lower by 6%. Texture had no effect on the coarse roots. Thefinertexture resulted in a 14% increase in the active soil C pool, less than a 1%) increase in the other soil pools, and a 5% decrease in soil respiration.  142  The output from the simulation using a coarser texture (e.g. 42% sand, 54% silt, and 4% clay) were similar in value but opposite in direction. The simulation with the coarser grouping resulted in a 5% increase in NPP, a 4% increase in foliage biomass and surface litter, and a 4%> increase in the biomass of fine roots and soil litter. In the model, greater production with the coarser soils is related to greater available soil moisture as a result of less soil water lost through evapotranspiration. This is due to an increased infiltration rate of the coarser textured soils. The coarser texture also resulted in a decrease of 14%> in C in the active soil pool, with less than a 1 % decrease in the other soil pools. It resulted in an increase of 4% in soil respiration. The lower amount of C in the active soil pool of the coarser grouping is a result of a higher turnover rate of the active soil organic matter (Metherell et al, 1993). In the sensitivity analysis, the finer grouping had a higher soil C content. This is related to the findings that the active soil organic matter, which enters the slow soil pool, has a higher stabilization rate in finer textured soils than in coarser textured soils (Metherell et al, 1993). This is the only set of sensitivity analyses in which NPP and C in storage increased and soil C decreased. In the sensitivity analyses discussed earlier, the parameters either all increased or decreased. In this situation, soil C, biomass production, and C in storage showed opposite trends. This effect of texture on soil C has been previously documented (Lai et al, 1995). Thus, the model is simulating natural systems reasonably well. The high sensitivity of the model to soil texture is indicated by the observation that the differences which occurred in the sensitivity analyses occurred on soils within the same textural group (silt loam). Larger effects can be expected when comparing across textural groups.  143  4.4.1.6.4 Carbon:Nitrogen Ratio The model allows for inputs of the C:N ratio of the soils. A review of the default data sets indicated very different C:N ratios between forest and agricultural soils for the three different soil pools. The forest soils had a higher C:N ratios compared to the agricultural soils (Appendix C). The C:N ratio for the active soil pool of the forest soils was 15 compared to 10 for the agricultural soils and 32 for the slow pool of the forest soils compared to 17 for the agricultural soils. The C:N ratio was and 18 for the passive pool of the forest soils compared to 8 for the agricultural soils. To estimate the influence of this parameter, sensitivity analyses were carried out by substituting the C:N ratios of the forest soils with those of agricultural soils in the forest simulation. The results indicate the C:N ratios of soils have a large impact on the output (Appendix C). With the substitution of the forest C:N ratios with the lower C:N ratios of the agricultural soils, the forest total NPP increased by 18%. This was attributed to the greater amount of N mineralization through decomposition in soils with the lower C:N ratio. Carbon in the tree foliage and surface litter pools increased by 12-25% and in the soil litter and fine root pools by 16%). As the leaves and roots contribute to the surface and soil litter, the litter pools were expected to increase. The total C budget increased by 4%> and the soil C by 2%. Therefore, it is important that the C:N ratios of the soils are properly entered according to the land use. 4.4.1.6.5 Soil Drainage The model provides a parameter that indicates if the soils are subject to anaerobic conditions (high soil water content) as this causes decomposition to decrease (Metherell et al, 1993). The soils in the study area are silt loam in texture and overlie a coarse textured glaciofluvial deposit and thus they are well drained. Therefore, the 'drain' parameter was set at  144  0.9 indicating a low anaerobic condition. Sensitivity analyses were carried out on this parameter. In one test, the drainage parameter was set at 0.5. This resulted in a reduction in the total NPP of the natural forest by 70 g C m" yr" or 9%. When set at 1.0, the NPP increased by 5% 2  1  in comparison to using the base value of 0.9. Therefore, between 0.5 and 1.0, the total NPP was affected by approximately 14%. The closer the value was set to 1.0, the greater was the impact. That is, between 0.5 and 0.9, the NPP was reduced by 9% or 2% for each tenth of a value. However, set between 0.9 and 1.0, it was reduced by 5%. That is, 5%> per tenth of the value. Therefore, the effect when using 0.9, falls between 0.5 and 1.0. Thus, an error in this value will result in an error in the order of 9%. With hay, the total NPP was reduced by 11% when the input value was set at 0.5 compared with the findings when set at 0.9. The value of 0.9 therefore appears to be reasonable for this parameter considering the site conditions and the impact of the parameter. The user should ensure that this parameter is adjusted for the site. 4.5 RESULTS AND DISCUSSION  The detailed output of the simulations is included in Appendix C. 4.5.1  Natural Forest  The model simulation produced output for the total forest budget excluding a component for understory. In the C budgets discussed in Chapter 3, the understory represented 6% of the total NPP estimated for the natural forests in the Abbotsford area, and less than 1% of total C in storage (Table 3.23). That is, it appears that the model can simulate the major C pools in a forested system fairly completely, even with the understory not included.  145  4.5.1.1 NPP The simulation resulted in an average NPP over the 20-year period, of approximately 870 g C m" yr" (Table 4.2). The NEP was estimated at 355 g C m" yr"'. 2  1  2  Table 4.2 Comparison of simulated and estimated/observed N P P of a natural Douglas-fir forest and simulated N E P . Pool  Simulated (average) Estimated/ Difference Observed gCm yr' g Cm' yr' % 2  Tree Coarse roots Fine/small roots (Total roots) TOTAL Decomposition respiration NEP* * NEP: net ecosystem production  2  550 70 250 (320) 870 515  525 70 180 (250) 775  1  +5 0 +38 (+28) 12  355  The NPP output is one means of evaluating the performance of the model. A comparison was made between the simulated NPP and the NPP budget presented in Chapter 3 (Table 3.23), which was used as a starting point for the simulation. The model overestimated the total NPP in the budget discussed in Chapter 3 by 12%, which represents a difference of 95 g C m" yr" . On a component basis, it overestimated aboveground NPP by 5%> or 2  9  1  1  25 g C m" yr" . It also overestimated the fine/small roots by 39%. There is no difference in the coarse root component. 9  1  A difference of 25 g C m" yr" in the aboveground NPP represents 0.0001% of the aboveground portions of the tree (Table 3.23). Over 20 years, this represents an additional >y  500 g C m" or 0.03%> in the aboveground portions of a tree. This indicates that the model is operating well for this part of the budget. Peng et al. (in press) also found that the C E N T U R Y model simulated aboveground biomass "acceptably" well on boreal forests. It should be 146  recalled that for this study, the estimated aboveground NPP (Table 3.23) was based on an average of the results of a high and low productivity site investigated by Keyes and Grier (1981). The input for the simulation was based on a maximum potential monthly NPP, which also was estimated and based on the most productive site investigated by Keyes and Grier (1981). The assumption was that the input would be reduced by site limitations. Even with these estimates and expectations, the NPP simulated was within the maximum upon which it was based and only 5% above what was expected. The model appears to be simulating tree coarse roots very well. The model over estimated fine/small roots by 39% or 70 g C m" yr". This is not unexpected considering that 2  1  fine roots were sampled in the winter period, the time of lowest root biomass. Even though fine/small roots represent 22% of the NPP (Table 3.23), they represent less that 1 % > of the total C budget (Table 3.23). This indicates that fine/small roots are not major C storage pools. Thus, discrepancies on the order of 70 g C m" will not have a major impact on the C budget 2  and, therefore, does not seriously detract from using the CENTURY model for this purpose. Based on the results of the simulation studies, the basis of the input data and the assumptions related to the data (as described in terms of the sensitivity analyses), as well as the assumptions of the underlying operation of the model, are acceptable for describing the natural forest in our study area. It implies a valuation on the quality of the input data and the model design and operation. Thus, the model appears to be suitable for simulating natural secondgrowth Douglas-fir forests in our study area.  147  4.5.1.2 Carbon in Storage  Averaged over the 20-year period, total C of the natural forested sites was approximately 32,900 g C m". The aboveground components of the trees represent 58% of the 2  total budget, with <1%> of that in leaves and 2%> in branches (Table 4.3). Coarse woody debris contributed 7% C to the budget, coarse roots 11%, and soil 20%>. All other components contributed <1%> of the total budget. In the soil pools, the active pool contributed less than 1% of the total budget, and the slow and passive pools contributed 13% and 6%>, respectively. These findings indicate the dominant C pools and thus, the components of focus.  Table 4.3 Simulated carbon budget of second growth Douglas-fir forest. Storage average 60-Years-Old % Change* ("50"-Years-Old) (40 to 60-years) gCm % of total gCm  Pool  2  Leaves Branches  240 505  Wood (Tree-aboveground)  <1 2  210 255  -49 -74  18,500  56  20,460  +26  (19,245)  (58)  (20,925)  3,575  11  3,820  (+19) +17  415  1  Tree coarse roots Tree fine/small roots  405  0  (3,990)  (12)  (4,225)  (+15)  (23,235)  (71) 7  (25,150) 3,115  (+18) +256  (Total roots) (Total tree- above + below)  2  Coarse woody debris  2,190 360  Surface Litter Surface Microbial  100  1  255  -73  <1  90  -40  Soil litter  380  1  405  +119  Soil - active pool  240  <1  260  +49  Soil - slow pool  4,370  13  4,890  +33  Soil - passive pool  2,070  6  2,075  <+l  (6,680) 32,945  (20)  (7,220) 36,235  (+22) +23  (Total soil) T O T A L carbon * R A f p r fr\ citYinlntirvn mitrviit  A n n p n r l i Y ("*  The data presented in Table 3.23 presents the C budget of a 40-year-old forest. The simulation results in Table 4.3 include the average, which represents a 50-year-old forest, and 148  after 20 years, a 60-year-old forest. The long-term trends indicate that second-growth Douglasfir forests are net accumulators of C, i.e., C sinks, increasing by approximately 23% over the 20-year period, or just over 1% a year. The amount of C in the stemwood increased by 26%> over the 20-year period (Figure 4.3). Branch and needle biomass decreased by 74% and 49% respectively, at a greater rate in the first several years of the simulation, after which these pools attained a steady state (Figure 4.4). Such decreasing trend with the branch and foliage pools seems suspect however, these two pools together represent less than 3% of the total C budget and this should therefore not detract from the usefulness of the model for this study.  25000 20000  Carbon  (9 Cm )  15000  • Stemwood  2  10000 5000 0  H H—h  H  1  9  1  1  1  11  1  13  h  H 15  17  1  h 19  Time (years)  Figure 4.3 Forest ( 2  n d  growth Douglas-fir) stemwood over time.  Branch mortality in Douglas-fir forests has been previously documented (Gessel and Turner, 1976; Maguire, 1994). The rate of branch mortality is positively correlated with the size of trees and tree density ( Maguire, 1994). Maguire (1994) found that the maximum branch size at the crown base of Douglas-fir trees increases with increasing diameter at breast height (DBH), with larger trees shedding branches of larger basal diameter. Therefore, branch  149  loss and correspondingly needle biomass in the simulation, is consistent with trends reported in the literature, however, the rate simulated by the model appears high. Coarse woody debris increased by 256% over the period of the simulation. Coarse woody debris included material from the branches, stemwood, and coarse roots (Figure 4.5), averaging 16%> in branches, 68%) in stemwood, and 16% in coarse roots (Appendix C).  1200 1000  • Leaves • Branches  800  • Litter  Carbon (g C m ) 600 2  400  f  200 0 9  1  11  13  15  17  19  21  Time (years)  Figure 4.4 Forest (2  n  growth Douglas-fir) - branches , leaves , and litter.  Surface litter decreased by 73%> or by 690 g C m" over the 20-year period. It decreased 2  from 945 g C m" at the beginning of the simulation to 255 g C m" at the end. 2  2  Similar to the pattern of the foliage and branches, it decreased rapidly in the early years of the simulation and then reached a steady state (Figure 4.4). The leveling off of the litter is a response to a steady but lower source of litter, i.e., foliage. As shown in Figure 4.5, the only input into the surface litter pool is from the tree leaves (needles). The decrease in litter fall coincides with the decrease in foliage and branches (Figure 4.4). Prescott et al. (1996) found 150  LEAF  RLEAVC RLEAVE(1-3)N,P,S  PLANT PRODUCTION  : Annual Leaf Production Fine FROOTC ; Annual Root Production Fine FBRACC : Annual Branch Production RLWACC • Annual Large Wood Production CRTACC • Annual Coarse Wood Production FCACC • : Annual Total Tree Production SUMRSP •: Monthly Maintenance Respiration RLVACC  RESIDUE  Metab. Struc.  PPT TEM FINE ROOTS  L = Lignin NL = Non-Lignin PPT = Monthly Precipitation TEM = Monthly Soil . Temperature  SURFACE  FROOTC FROOTE(1-3)N,P,S  FINE BRANCHES  FBRCHC FBRCHE(1-3)N,P,S  LARGE WOOD  RLWODC RLW0DE(1-3)N,P,S  ROOT RESIDUE  Metab. Struc. ACTIVE SOM  DEAD FINE BRANCHES  WOOD1C WOOD1E(1-3)N,P,S  DEAD LARGE WOOD  WOOD2C WOOD2E(1-3)N,P,S  SLOW  V  SOM  co„  COARSE ROOTS  CROOTC CR00TE(1-3)N,P,S  DEAD COARSE ROOTS  WOOD3C W00D3E(1-3)N,P,S  Figure 4.5 Carbon flows of forest submodel (Metherell et al., 1993).  that 35% of Douglas-fir litter decomposed in one year. With this decomposition rate and the decrease in inputs, the litter is expected to decrease. Gessel and Turner (1976) measured litter production on second growth Douglas-fir trees and found that annual leaf litter production increased up to 40-years of age and then became fairly constant. At the same time they reported an increase with time in woody material fall, which included branches. This resulted in a 243%o increase in woody material between their 40-year-old plots and their 50-year-old plots. In this study, over the 20-year period of the simulation, wood CWD increased by 412% (Appendix C). Branch CWD decreased by approximately 23%, similar to the trends found by  151  Gessel and Turner (1976). It would seem that if the number of branches falling off increased with time, the amount of litter would decrease with time, as the source of litter from living branches would be reduced. However, Maguire (1994) found that large trees have a higher branch density per unit stem length and the number of branches lost during crown recession is greater. He noted that there is a relatively rapid rate of branch mass loss in larger trees in a stand. It would seem with more live branches overall, this may not affect litter fall. Therefore, there appears to be some inconsistencies in the literature concerning the trends of litter production with time. Basically, the model simulates litter to reflect an initial decrease, perhaps, as the initial input was too high for the other conditions of the forested ecosystem. The leveling off may indicate that the rate of accumulation was steady, similar to the findings by Gessel and Turner (1976). The amount of litter in the latter part of the simulation is low compared to the findings of the field investigation (Table 3.4). In the field study, litter measured approximately 2  1  1,000 g C m" yr" (Table 3.4). This was the value of litter used to initialize the simulation. z  1  Litter has been previously reported for second growth Douglas-fir trees in the Pacific Northwest at 615 g C m" (Cole et al, 1967) and 585 g C m" (Grier and McColl, 1971). 2  2  Although the average amount of litter simulated was approximately 360 g C m", which is 2  approximately 40% of that found in thefieldinvestigation and less that that reported in the literature, the impact of such a discrepancy is not large. The amount of litter at approximately 1,000 g C m" represents approximately 3%> of the total C budget developed during the field 2  program (Table 3.23). The simulated average litter amount of 360 g C m" would represent 1 % > 2  of the total budget, a discrepancy of 2% of the total budget.  152  The surface microbial pool represents less than one percent of the total budget. It decreased by 40% over the period of the simulation. It reached steady state consistent with the trends in litter. This is expected as the decomposition products from litter flow into the surface microbe pool (Metherell et al, 1993). Soil litter increased 122% over the period of the simulation. In the forest system, only roots contribute to the mineral soil litter pool (Figure 4.5). The increase in soil litter reflects fine/small root dieback. The NPP of fine/small roots was approximately 250 g C m" yr", 2  1  fluctuating throughout the simulation, i.e., from growing season to growing season, likely due to responses to weather effects, with fine root biomass averaging only 415 g C m" over the 202  year period (Figure 4.6). High rates of fine root dieback in Douglas-fir forests have been previously documented (Kurz, 1989; Keyes and Grier, 1981).  500  •  400 Carbon (g C m' ) 2  300  •  200  •  •  • Root NPP  • • • •  •  • Root Storage  100 0* 1972  1977  1982  1987  1992  Time (years)  Figure 4.6 Forest (2nd growth Douglas-fir) - fine/small root storage and NPP.  Total soil C increased by 22% over the period of the simulation (Table 4.3), just over 1%) a year. Cropper and Ewel (1984) found similar results in their model simulations of 40-60153  year-old Douglas-fir stands in the Pacific Northwest. Carbon in the active pool represented approximately 4% of the total soil pool. The active pool, which includes soil microbes, increased substantially, by 49%, however, this represented only 85 g C m" over the 20-year 2  simulation. The slow pool increased less, at 33%. However, this represents 1,200 g C m" over 2  the 20-year period. The slow pool represents 65% of the total soil pool. The source of inputs into both pools includes CWD (Figure 4.5), which increased by 256%> over the period of the simulation. Flows into the active pool also occur from the roots and soil litter (Figure 4.7). The slow pool also receives inputs from the active pool, the roots, the microbial pool, and from the surface litter (Figure 4.7). The passive pool was static over the 20-year period as expected. This pool has a turnover time of between 400 and 2,000 years (Metherell et al, 1993). Although NPP is an indicator of the net amount of C extracted from the atmosphere by vegetation some of this C is re-released as a result of microbial decomposition. Decomposition (heterotrophic) respiration averaged 515 g C m" yr" (Appendix C). The amount of C lost to 2  1  respiration is countered by the average NPP of 870 g C m" yr". This leaves a net amount of 2  1  355 g C m" yr" as an addition to the C pool of the forest system, that is, the 23% increase in 2  1  stored C in the forest, over the 20-year period, represents approximately 6,835 g C m" . Over 20 years, it represents an average annual addition to the forest system of approximately 340 g C m". Therefore, the NEP of the forest system is approximately 350 g C m" yr". This 2  2  1  indicates it is a net sink. This result is generally supported by others (Kaiser, 1998; Moffat, 1997; Kurz et al, 1995; Dixon et al, 1993; Rowntree and Nowak, 1991). It also suggests the model is simulating the natural system well.  154  Figure 4.7 Carbonflowsof the CENTURY model (Metherell et al., 1993). One important value of the simulations of the CENTURY model is that they provide a proximate value for the size of the sink from which to make comparisons. Moffat (1997) notes that improved use of forests worldwide could sequester enough C in soils, trees, and other vegetation between 1995 and 2050 to offset 15% of fossil fuel emissions during that period. It is deforestation that generally results in forest being C sources (Houghton, 1995; Sampson et al, 1993). Dixon et al (1993) note that forest systems provide multiple opportunities to offset or stabilize greenhouse emissions through the expansion of existing forests, the reduction in deforestation, and in the production of biofuels to offset fossil fuel combustion. The value of forests as C sinks and stores is a function of whether they are not harvested or, if harvested, if there is an improvement of the length of time wood products are preserved or whether there has  155  been an improvement in waste management (Heath et al, 1993; Thompson and Matthews, 1989). Sedjo and Solomon (1988) note that forests can postpone the build-up of C in the atmosphere and, thus, buy time for the substitution by non-fossil fuels as a more permanent solution to the global warming problem. Moffat (1997) refers to forests as greenhouse sponges noting that planting is only a temporary option to combating global warming as plantable land will eventually be totally utilized. MacKenzie (1994) notes that northern forests, through increasing growth, provide an explanation for the missing C sink. There is some concern that forests can be a C source. This is generally related to the boreal forests where temperatures have increased over the last century and are predicted to increase with global warming (Goulden et al, 1998; Sampson et al, 1993). This is primarily related to the release of C O 2 from seasonally and perennially frozen soils that contain one of the largest pools of C in the terrestrial biosphere (Goulden et al, 1998). However, this is countered by the increase in NPP of the vegetation - by how much is not evident. Peng and Apps (in press) used the CENTURY model to examine the sensitivity of the boreal forest ecosystem to climate change. They found that the combined increased temperatures and atmospheric C O 2 positively interact to increase NPP and decomposition rates and reduce soil losses. There appears to be less of this concern with temperate forests (Sampson et al, 1993; Dale and Franklin, 1989). 4.5.2  Urban Forest  4.5.2.1 NPP  The NPP of the urban forest averaged approximately 945 g C m" yr" (Table 4.4). The 2  1  NPP is higher in the urban forest than the natural forest by approximately 9%. This is  156  considered to be primarily a response to higher soil C, and, thus N, in the urban setting. In the sensitivity analyses described earlier, a natural forest simulation in which the forest soil C content was substituted with the urban soil C content which was 11% higher, resulted in an increase in the NPP of 5%. An explanation for the other 4% may be related to the difference in bulk density in combination with the effects of absent litter and coarse woody debris as these are the only other differences between the natural forest and the urban forest. On a component basis, the aboveground NPP in the urban forest simulation represented 63% of the total NPP. Fine roots represent a large portion of the NPP at 29% of the total. Coarse roots represent only 8% of total production. The NEP of the urban forest is 540 g Cm" . 2  Table 4.4 Simulated urban Douglas-fir forest NPP and NEP. Pool  Simulated (average) gCm yr %of total 2  1  Tree (aboveground) 600 75 Coarse roots Fine/small roots 270 (Total roots) (345) 945 TOTAL Decomposition 405 respiration 540 NEP* NEP: net ecosystem production.  63 8 29 (37) 100  4.5.2.2 Carbon in Storage  The C budget of the urban forest averaged approximately 31,000 g C m" (Table 4.5), 2  increasing by approximately 20%> over 20 years. The largest pools are the stemwood and coarse roots of the trees and the soil. The stemwood increased by approximately 29%> or 4,795 g C m" over the 20-year period. The leaves and branches decreased by 48% and 73%> 2  157  respectively. The aboveground component of the tree represented approximately 63% of the total budget with the stemwood being the dominant pool and representing 60% of the total.  Table 4.5 Simulated carbon budget of an urban forest. Storage Pool  (average 60-Years-Old % Change "50 years old"years) (40- to 60-years) gCm % of total gCm % 2  Leaves Branches Wood (Tree- aboveground) Tree coarse roots Tree fine/small roots (Total roots) (Total tree) C W D (Coarse roots) Surface microbial Soil litter Soil - active pool Soil- slow pool Soil- passive pool (Total soil) T O T A L carbon  255 515 18,845 (19,615) 3,635 445 (4,080) (23,695) 320 45 450 245 4,190 2,300 (6,735) 31,245  2  <1 2 60 (63) 12 1 (13) (76) 1 <1 1 <1 13 7 (22) 100  230 270 2, 135 (21,635) 3,940 445 (4,390) (26,025) 535 45 455 260 4,395 2,300 (6,955) 34,015  -48 -73 +29 +22 +20 no trend (+17) (+21) +970 -18 + 128 +33 +8 0 (+6) +20  The surface microbial pool in the urban forest decreased over the period of the 2  2  simulation by 18% or 10 g C m" . The initial C input into this pool was set at 20 g C m" as it was expected to be lower than a forested site. This is related to the fact that the trees are part of the lawn mosaic. The microbial population was expected to be lower in the urban forest as a result of a deficiency of organic additions in the form of CWD, litter, or any other organic amendments. The initial input was estimated. It is likely that the model increased this pool to 55 g C m~ in thefirstfew years of the simulation, likely as a response to the other site inputs, 2  i.e., the initial input value may have been underestimated. The microbial pool decreased from the initial response, gradually stabilizing to 158  45 g C m" by the 9 year of the simulation (Appendix C). This coincided with the trend in the 2  th  foliage pool that also decreased rapidly in the early part of the simulation, then stabilized by the 10 year. As the decomposition rate of the coarse woody debris of the aboveground th  components was set at zero, the only potential source of material to support the microbial pool would have been the leaf litter. As seen in Figure 4.8, following an increase in both components in the early years of the simulation both the leaves and microbial pool fluctuated but with no overall trend, up or down. The fluctuation from year to year indicates the effects of annual climate differences. The microbial pool represents a minor component of the total C budget at less than one percent. •y  Tree coarse roots increased by 20% or 645 g C m" over the period of the simulation, representing 12%> of the budget. Similar to the natural forest simulation, no trend occurred with fine roots, as expected. Fine roots represent 1%> of the total budget, at 445 g C m". Although 2  they contribute 29% to the total NPP, the greater part of the C sequestered annually by fine roots at 270 g C m" yr" is not transferred into long-term storage. 2  1  300 • Leaves  250  • Microbial  200 j  •  Carbon (g C m ) 150 2  •  *•  •  •  •  •  •  •  • •  100 50 II  0 9  11  13  15  17  19  21  Time (years)  Figure 4.8 Urban forest - leaves and microbial pools.  159  The CENTURY model provides for coarse root dieback as coarse root CWD. Coarse •y  root CWD increased by 970% or 485 g C m" over the 20-year simulation, averaging approximately 25 g C m" yr". This pool represents only 1%> of the budget. Considering the 2  1  physical constraints of measuring this component and the low amount of carbon it contributes to the C budget (1%), it is reasonable to estimate it. Soil litter increased with time, by approximately 128%) or 255 g C m" (Appendix C). 2  Soil litter represents one percent of the budget. The inputs into the soil litter are from the fine root pool (Figure 4.5). As the fine roots die on an on-going basis, it was expected that the soil litter would correspondingly increase. The C released from the coarse root pool, as a result of death, inputs directly into the soil active and slow pools (Figure 4.5). Carbon in the soil increased by 6% over the 20-year simulation. The active pool increased 33%> (Table 4.5), by approximately 65 g C m" over the 20-year period. Inputs into 2  this pool are from the roots and root CWD (Figures 4.5 and 4.7). This pool represents less than one per cent of the budget. The slow pool is much larger, representing approximately 13%> of the total budget and 62% of the soil C. The C in the slow pool averaged 4,200 g C m". It 2  increased by 8%> over the 20-year simulation, which represents an increase in C of 320 g m" or 15 g C m" yr". The slow pool receives C from many pools including the soil litter, the 2  1  microbial pool, and from dead roots (Figures 4.5 and 4.7). The passive pool did not change over time, as expected. Similar to the discussion above for the natural forest, there is a concern whether this system is a C source or sink. Even though the litter and the CWD-C storage pools were not part of the budget in the urban forest system, it appears that the urban forest is also a C sink.  160  The CENTURY model simulates soil respiration (Figure 4.7). For this simulation, the NPP of the system was in the order of 945 g C m" yr" (Table 4.4) and respiration averaged 2  2  1  1  405 g C m" yr' (Appendix C), suggesting a net sink. The increase in assimilated C by 20% over the 20-year period (Table 4.5), also suggests that these forests are C sinks. Therefore, it appears that the urban forest, that is, trees growing in an urban system, is a net C sink. Trees can be used in an urban area to counteract global warming by increasing the C sink over time. This has been previously recognized (McPherson, 1994; McPherson et al, 1993; Rowntree and Nowak, 1991). The estimation of annual C sequestration rates for urban trees, the evaluation of planting trees to offset C O 2 emitted through heating and air conditioning of buildings, and the reduction of building operating costs associated with the strategic placement of trees as windbreaks and shade providers, has also been investigated (Rowntree and Nowak, 1991). As well, city tree planting programs such as the Tree Cities program of Chicago, Illinois, USA are being adopted to counter global warming (McPherson et al, 1993). 4.5.3 Hay  4.5.3.1 NPP The simulation indicated the average NPP of hay over the 20-year period was approximately 640 g C m" yr" (Table 4.6), excluding the NPP of cultivation years (years 1, 9, 2  1  and 17). The NEP was estimated at 145 g C m" yr" .  161  Table 4.6 Comparison of simulated and average observed NPP of hay crops and simulated NEP. Pool  Simulated Observed Difference (average) g Cm' yr' gCm yr' % 2  1  2  Aboveground 315* 395 Fine Roots 325* 95 TOTAL 640* 490 Decomposition 495* respiration NEP** 145* * Values reflect average of all years except cultivation years; ** N E P : net ecosystem production.  -20 +242 + 131  The simulated aboveground production is 20% less than the average NPP measured in the field program (Table 3.25), a difference of 80 g C m" yr" . The input for production is the maximum monthly production that should reflect aboveground production in optimal summer conditions (Metherell et al, 1993). This value is affected by temperature, precipitation, and the nutrients of the site. It was estimated from the most productive site, Site A4, at 520 g m "  2  biomass aboveground per month. (The NPP input for grass is based on biomass rather than on a C basis.) As noted above, the aboveground NPP parameter is considered to be the parameter that can be used to calibrate the predicted crop production for different environments, species, and varieties (Metherell et al, 1993). Considering all of these factors, including the devised management program, the difference of 20% or 80 g C m" yr" in the aboveground annual 2  1  production indicates that the model simulated hay production in the study area reasonably well. The simulated aboveground production was within 15 g C m" yr" of the observed NPP of two 2  1  of the four farms. The model simulation overestimated (fine) root production by 242%, a difference of 230 g C m" yr". In the model, root production is controlled by annual precipitation. To 2  1  account for winter dormancy, the root-shoot ratio does not change in months when soil 162  temperature falls below 2° C (Metherell et al, 1993). In the simulation, root production was approximately equal to aboveground production. This close relationship between the aboveand belowground production has been previously reported (Christian, 1987; Walton, 1983). Christian (1987) notes that roots may comprise over half of the total weight of the plant. He notes that the patterns of root growth are poorly understood due to the difficulty of measuring roots. Grass roots are subject to severe dieback (Christian, 1987; Walton, 1983) and Christian (1987) noted that the difficulty of measuring grass roots is related to the on-going phenomena of their death and replacement. Defoliation also affects root growth (Walton, 1983). Roots in thefieldstudy were measured in late winter. This represents a period of low root biomass as it followed root dieback that likely occurred in the hot, dry periods of the latter part of the summer and before maximum root production and accumulation could occur in the early summer of the next year. The CENTURY model operates on a monthly time step. Thus, it calculates root production month by month and accumulates it to produce an annual rate. By this, it captures the periods of high as well as low growth. Thefieldmeasurement only included low growth. This suggests that roots should be measured throughout the year: the winter, early summer, and late summer, reflecting the range from maximum to minimum. 4.5.3.2 Carbon in Storage  The C budget of hay only increased by 2% over the 20-year simulation (Table 4.7). With the management applied, the hay system was a sink but with only a 2% increase, or 0.1% per year. This suggests the C budget of a hay system, as simulated, is maintaining its C in a steady state. Total C in storage was approximately 10,000 g C m" at the end of the 20-year simulation. Of this, 88% of the total C budget is allocated to the soil pool indicating the importance of the soil as a major C storage pool. The simulation not only indicated the long 163  term trends but, as well, the effects of cultivation and seeding on the various C pools and of using manure as an amendment. Table 4.7 Simulated carbon budgets of hay land use. Pool  Storage - Manure -Chemical Fertilizer Difference Storage after 20 years after 20 years Manure vs. (average) (average) Chemical gCm % change gCm % change %of %of % change total total end of sim. 2  Stubble  2  135* 1 no trend 115* 1 no trend +17 (135*) (115*) (1) (1) Surface litter 395** 4 +133*** 2 +182 140* +65*** (365*) (170*) (2) (4) Roots 305* 3 no trend 265* 3 no trend +15 (305*) (265*) (3) (3) <1 Microbial 60** +50** 25** <1 -67** +140 *** *** (60*) pool (30*) (<D (<1) Soil litter 275** 3 +67* 210** 2 +20** +31 *** *** (245*) (205*) (2) (3) Soil - active 315 3 +19 270 3 +45 +17 pool (325) (295) (3) (3) -4 Soil - slow 5,235 54 4,600 53 -16 +14 (54) pool (5,220) (4,930) (54) <+l <1 Soil-passive 3,075 31 0 3,070 35 (32) (3,075) (34) pool (3,075) (Total soil) (88) -2 (7,940) (91) -10 +9 (8,625) (8,620) (89) (8,300) (91) +2 TOTAL 9,795 100 8,695 100 -8 +13 carbon (9,725) (9,0985) (100) * Values reflect all years except cultivation years (year 1, 9, 17); ** The value in these pools reflects the value at the end of the 20-year simulation which was only in the 3 year of the last rotation. Values are higher towards the end of each rotation, being interrupted by cultivation; *** l year not included as cultivation year. rd  s l  Cultivation greatly affected the stubble, surface litter, microbial, and root pools. Cultivation resulted in a decrease in these pools during those years (years 1,9, 17) (Appendix C). It, however, resulted in an increase in the soil litter, in the active and slow soil pools, and in microbial respiration. Stubble (which included the aboveground live and standing dead material) represented 1% of the total C budget, averaging 135 g C m", (not including the cultivation years). There 2  164  was no trend in the stubble pool from year to year, except in the years of cultivation when annual growth was diminished at the beginning of the growing season during new seedling establishment. Carbon simulated in the stubble pool was 35 g C m" higher than the average 2  measured in the field program, at 100 g C m" (not including the mosses). However, this value 2  of 135 g C m" was within the range found in the field program (Table 3.7). The model appears 2  to be operating well to simulate C in stubble in the same ranges as observed in the field program considering the hay simulation was based on a designed management regime which included cultivation, seeding, and the application of chemical and organic amendments, and an estimated maximum monthly input value based on the site of maximum production. Surface litter was very high at 395 g C m" by the end of the 20-year simulation. It had 2  increased by 133% over that period. The surface litter represented 4% of the total C budget. At cultivation, surface litter decreased by approximately 58%>. The model operates such that with cultivation, C in the surface litter is transferred to the surface microbial, slow soil, and soil litter pools (Figure 4.7). Carbon from the soil litter pool is transferred to the active soil pool. At cultivation, spikes occurred in the soil litter and soil active pools as a result of the incorporation of the stubble and surface litter. This provided a substrate source for the soil microbes. This incorporated material underwent rapid decomposition as a spike in the soil respiration rate also occurred at the time of cultivation (Appendix C). Following the reseeding year, the surface litter resumed accumulation. The value at the end of the simulation represented the 3 year of rd  an 8-year rotation. A review of the detailed output (Appendix C) indicates that if the simulation had ended at the end of the rotation, surface litter would have been higher, having more years to accumulate. This illustrates the effect of rotation length of the, suggesting that with a longer rotation, C storage would increase.  165  The surface microbial pool showed the same trends as the surface litter. It increased with time, increasing each year up to the re-seeding year. In the re-seeding year, it was greatly reduced, by approximately 68%. It then began to accumulate again, increasing by 50%> over the 20-year simulation as the surface litter accumulated. The model operates such that the C from the surface microbial pool is transferred to the slow soil pool. Similar to the surface litter, the value at the end of the simulation represented the 3 year of an 8-year rotation. A review of rd  the detailed output (Appendix C) indicates that if the simulation had ended at the end of a rotation, the surface microbial pool would have been higher, having more years to accumulate without interruption. Cultivation resulted in a decrease in the surface microbial pool as expected as the surface material is incorporated into the soil at ploughing. The microbial pool decreased to approximately 25 g C m" during the cultivation years, tripling by the end of a rotation. The surface microbial pool represented less than 1% of the total C budget. Cultivation resulted in a greater than average increase in soil litter, compared to the rest of the rotation, as expected. The CENTURY model is designed such that the inputs to soil litter at cultivation are from the roots, stubble, and dead material on the surface, and from the roots during non-cultivation years (Figure 4.7). The model is designed such that C is transferred from the soil litter to the active soil pool, which also increased at the time of cultivation. This is attributed to the new additions of substrate that would support an increased soil microbial population. As noted earlier, the model operates such that the roots contribute on a continuous basis to the soil litter (Metherell et al, 1993). A review of the simulation results indicates that the model accommodated root dieback as C assimilated annually in the roots exceeded C stored in the roots. The model predicted a net root production averaging approximately  166  325 g C m" yr" (Table 4.6), not including the cultivation years. However, C in storage in the 2  1  roots averaged 305 g C m" , not including the cultivation years. This phenomen simulates findings in natural systems (Christian, 1987; Walton, 1983). The soil C pool represented the largest component of the budget averaging approximately 8,900 g C m" or 89% of the total budget. Soil C, however, decreased with time. It decreased by 2%> or 165 g C over the 20-year period, for a loss of 8 g C m" yr". The C in the 2  1  passive pool remained relatively unchanged with an increase of <1 %. The C in the active soil pool increased by 19%, 50 g C m" over the 20-year period, and decreased by 4%> or 215 g C m" in the slow pool. The active pool represents only 3%> of the total C budget 2  compared to the slow pool, which represented 54%. Therefore, the greatest change occurred in the slow pool, with the additions in the active pool not large enough to compensate for the losses which occurred in the slow pool. A review of the detailed output indicates however, that the greatest decrease in soil C in the slow pool occurred at the beginning of the simulation, suggesting that the model adjusted this pool to reflect the other parameters in the simulation. Following this decrease, soil C fluctuated on an annual basis, not increasing or decreasing. Cultivation affected the active and slow soil pools (Appendix C). The active C pool increased by approximately 13%, temporarily for that year. This is considered the result of soil microbes responding to the new substrate additions, that is, to the incorporation of stubble, surface litter, and roots into the soil (Figure 4.7). This pattern also occurred in the slow soil pool which is directly affected by the active pool (Figure 4.7). The short duration suggests that the fresh material underwent rapid decomposition in the same year. This is supported by the fact that decomposition respiration increased beyond the normal trend in the three cultivation years at 620 g C m" yr", and decreased to less than the average of the non-cultivation years at 2  1  167  1 the following year (Appendix C). Cultivation increases the accessibility of 495 g C m"2 yr" organic matter to microbial attack (Voroney et al, 1981). As the land is immediately re-seeded following cultivation, the effects of cultivation on the soil were minimized. This was supported by the finding that microbial respiration reverted to non-cultivation levels in the other years of the rotations, as the microbial populations returned to normal levels. If cultivation had been an annual event, the high release of C from the soil with each cultivation event, may have resulted in a greater decrease in C with time. This is likely what occurs with annual crops. Cultivation does result in a reduction in soil C over time (Kern and Johnson, 1993; Mann, 1986). These agricultural practices have been identified as contributing a substantial flux of C O 2 to and the aggravation of the greenhouse effect (Kern and Johnson, 1993; Eriksson, 1991). The use of manure has been recognized as a means of increasing soil C (Lai et al, 1995; Cole et al, 1993; Brady, 1990). From this, it also follows that straw-based manure should result in a build-up of surface and soil litter and a substantial increase in the microbial pool, and therefore, in C storage. A program in which the fields are immediately re-seeded and manure is added on a regular basis, should allow for the maintenance of the soil C pools or close to it. As noted earlier, the hay simulations were run on an 8-year rotation with the 20  th  year ending part way through the third rotation. As such, the amount of C in storage increased over time from approximately 9,840 g C m" by the end of the 1 rotation, to 9,940 g C m" by 2  st  2  the end of the 2 rotation (Appendix C). If the rotations had been longer, it is predicted that C nd  in storage at the end of the simulation period would have been higher as high respiration rates occurred in the re-seeding years. The model predicts that the farther along in the rotation, the greater the amount of C stored.  168  The surface litter generated by the model, representing 4% of the total C budget (Table 4.7), was very high. Surface litter was not accounted for in the field program as all cut hay was picked up during the clipping program. A review of the detailed output (Appendix C) indicates that surface litter increased by approximately 180% between rotations and decreased by approximately 58% at each cultivation. Over the 20-year simulation, there was a net increase of 133%). The model is designed such that on-going inputs to the surface litter are generated from standing dead material which falls to the surface over time (Metherell et al, 1993). However, the amount of surface litter which accumulated on an annual basis from the standing dead material was much greater than the standing dead material could likely supply (Appendix C). Therefore, the high amount of surface litter was from another source. It is suspected that the source of the surface litter may have originated from the manure applications. Therefore, a simulation was carried out in which the annual manure application was replaced with a chemical fertilizer treatment (applied at a rate to achieve 75%> of maximum production). The results of the simulation in which the manure amendment was substituted with the chemical fertilizer suggest that the manure may be a source of the apparent excess surface litter (Table 4.7). The actual values of the C budget generated by the model are of less interest than the trends of the output. The differences in the various C pools of the manured and the chemically fertilized simulations were generally 15%>, being less in the chemically fertilized simulation. These differences could likely be reduced by the addition of an increased amount of fertilizer. The fact that litter accumulates on the surface indicates that decomposition of this material occurs at a relatively low rate. Surface litter increased over time, for both simulations. However, the surface litter increased by 133%) over the 20-year-run in the manured simulation, but it only increased by 65% in the chemically fertilized simulation. This could be interpreted  169  as a response to the greater amount of stubble, i.e., standing dead material, of the manured system over the chemically fertilized simulation. However, a comparison between the two systems indicates that the difference between the stubble is only 17%, but the difference between the surface litter is 182%. This suggests that approximately 70%> of the increase in surface litter is related to the manure. The manure applications in the model are a straw/manure mix and it is likely that the straw is contributing to this parameter. Stewart (1993) notes that under no-tillage systems, crop residues decompose slower because most remain on the soil surface where there is less biological activity. Therefore, such residues are important in terms of managing for the retention of C. The lower amount of decomposition reported on crop residues may also occur with the residues from the manure. Although the surface litter in the simulation of hay with a manure application, increased by a large percentage by the end of the simulation, the contribution if this C pool to the total C budget is small, representing 4% of the total budget. The increases in the soil litter and active soil pools over the period of the simulation were much greater in the manured simulation compared to those that occurred in the chemically fertilized simulation. This was expected as the sources of the soil litter and the active soil pool include not only the manure as an entity, but the roots and a portion of the stubble, which was greater in the manured simulation. Therefore, less substrate for the soil microbes would be available in the chemically fertilized system. As well, the fact that the manure is organically based should result in an increase in soil C (Lai et al, 1995). The difference in the surface microbial pool between the two management scenarios is large. It is greater in the manured system by 140%) compared to the chemically fertilized simulation. Greater surface microbial populations are expected with the manured simulations as  170  populations of microbes increase when fresh, decomposable material, such as manure, is added to the soil (Brady, 1990). Although cultivation resulted in a large decrease in the surface microbial pool, the size of the pool increased into the rotation. This suggests that management through increasing the rotation length can result in a larger surface microbial pool, allowing more time for C to accumulate. A comparison of the trends of each system over time suggests that manure had a large impact on C storage generally, except in the soil passive pool where little change occurred. The trends in C stored in surface litter of the manured system exceeded that of a chemically fertilized system by 68%, the soil litter by 47%>, and the soil active pool by 17%>. The trends in these pools far exceeded the general trend of the other parameters in the budgets. In both the manured and chemically fertilized hay simulations, soil C in the slow pool decreased over time but the decrease was less in the manured simulation at 4%, in comparison to a decrease of 16% in the chemically fertilized system. As discussed earlier, the decrease in the slow pool occurred in the first few years of the simulation after which it fluctuated with no trends and had reached a steady state (Figure 4.9). The initial decrease in the soil C in the slow pool indicates the model was adjusting this value to accommodate the simulation conditions. This suggests that the model was not simulating the hay system at the observed level in the beginning of the simulation. That is, the model is underestimating the soil C in the slow pool at approximately 4% less than the observed values. This difference of 4% between the simulated and observed amount of C indicates good agreement with the original data. Patwardhan et al. (1995), in their validation analysis of the capability of CENTURY for simulating soil C under varying agricultural management systems concluded that the CENTURY model "adequately represented" the dynamics of soil C. Parton and Rasmussen (1994), in their validation study  171  found that CENTURY can predict soil C changes within ± 5% on wheat-fallow agricultural systems. Jacques Whitford Environmental Ltd. and U. Saskatchewan (1999), in their planned research for continued validation of the CENTURY model, indicate that a 20% level of confidence is required in soil estimates of C sequestration. The active pool in the manured system increased with time, with the slow pool reaching a steady state, and with C in the other components of the budget increasing with time, it is predicted that under the type of management simulated the size of the C sink of the manured system would increase with time. In the chemically fertilized hay simulation, C in the slow pool steadily decreased. The difference between the two suggests that the manure stabilized the C in the slow pool. In the simulation with the application of chemical fertilizer, C in the slow pool had not yet reached steady state by the end of the simulation and it is not obvious when that would occur.  5600 5400 5200 5000 Soil Carbon 4800 (gm^)  4600 4400 -f  • Manured  4200  • Chemical  4000  0  2  4  6  8  10 12 14 16 18 20  Time (years)  Figure 4.9 Soil carbon in slow pool - manured vs. chemically fertilized hay.  172  The model can be used to predict whether hay production in the study area is a C source or sink. Agricultural systems can be considered C sources or sinks depending on management, with no tillage systems generally considered to be sinks and cultivated systems to be sources (Houghton, 1995; Lai et al, 1995; Cole et al, 1993; Stewart, 1993). It appears that a manured hay system in the study area is a C sink as the amount of C in the system increased with time. The hay system did become a C source in the re-seeding years when decomposition respiration exceeded NPP. Decomposition respiration exceeded the NPP by 145 g C m" in the first 2  cultivation year, by 205 g C m" in the second cultivation year, and by 425 g C m" in the third 2  2  cultivation year, averaging 240 g C m" (Appendix C). Respiration averaged 515 g C m" for 2  2  all years in the manured system compared to 425 g C m" in the chemically fertilized system. 2  This is likely the result of a greater amount of substrate in the manured system available to a higher microbial population. The increased decomposition respiration in the manured system was roughly countered by an increase in NPP, averaging 605 g C m" yr" (averaged for all 2  1  years), higher than that of the chemically fertilized system at 535 g C m" yr" (Appendix C). 2  1  The manured system resulted in an increase in C assimilation of 2% over the 20-year period compared to a decrease of 8% over the same period with the chemically fertilized system. This indicates that the manured hay system is a C sink and the chemically fertilized hay system is a C source. If hay production is the land use selected, every effort should be made to use a manure based system. At the end of the 20-year simulation, the C budget of the manured system had sequestered 1,100 g C m" more than the chemically fertilized simulation. The chemically fertilized system lost 755 g C m" over the 20-year period, approximately 40 g C m" yr". 2  2  1  These decreases occurred in the slow soil pool, which was reduced by 16% over the 20-year  173  period. The only pool in the manured system that decreased was also the slow soil C pool, which decreased by 4% over the 20-year period. The gains in the other pools in the manured system resulted in the manured system becoming a net sink. The soil C in the slow pool stabilized. This suggests that over time, the sink potential of a manured hay system may increase. The effects of the manure versus the chemical fertilizer and of the rotation indicate that the model is a useful tool in estimating the long-term effects of different management scenarios on C assimilation and storage. Also, the effects of the manure indicate that hay systems can be managed to increase the C storage and therefore have an effect on reducing atmospheric C O 2 . The simulation using a chemically fertilized hay system suggests that this is a non-sustainable practice in terms of C storage. 4.5.4  Lawn  4.5.4.1 NPP  The NPP of the lawn system averaged 375 g C m" yr" and the NEP was low at 2  1  65 g C m" yr" (Table 4.8). 2  1  Table 4.8 Lawn NPP simulated versus observed and simulated NEP. Pool  Simulated (average) g Cm yr  Aboveground Fine Roots TOTAL Decomposition respiration NEP* * NEP: net ecosystem production.  185 190 375 310  Observed (average) gCm yr 2  1  60 55 115  Difference % 208 246 226  65  174  9  1  Aboveground production averaged approximately 185 g C m" yr" . This value is 0  1  approximately 208% or 125 g C m" yr" greater than that measured at study area, at 60 g C m" yr" (Table 3.7). However, as discussed earlier, the simulation was based on the 2  1  highest monthly production found in the field program, at Site U3-plot and annual production at this site was at 145 g C m" yr" (Table 3.7). Aboveground production ranged from 2  1  20 g C m" yr" to 145 g C m" yr", with the wide range indicating the effect of management on 2  1  2  1  productivity (Table 3.7). A comparison between the NPP predicted by the model and the NPP upon which it was based (145 g C m" yr") indicates that the model only overestimated the 2  1  aboveground NPP used to initialize the simulation by 40 g C m" yr". 2  1  A review of the detailed output indicates the aboveground NPP of lawn fluctuated from year to year between 140 g C m" yr" and 420 g C m" yr", a range of 280 g C m" yr" during 2  1  2  1  2  1  the simulation. This indicates the sensitivity of aboveground production to climatic factors, the only inputs that varied on an annual basis during the simulation period. It should be kept in mind that the model simulation was based on an artificial management regime and adjusts the output according to the site limitations, including climatic factors. In the simulation, the lawn was fertilized in June of each year and was also cut on a monthly basis (the minimum allowed by CENTURY), although the lawns in the study area were mowed one or more times per week. As noted earlier, it is the potential aboveground maximum monthly NPP input parameter which is used to calibrate the predicted crop potential production for different environments, species and varieties (Metherell et al, 1993). With a discrepancy of only 40 g C m" yr" from the site 2  1  upon which it was based, the discrepancy appears to be within a reasonable range and suggests that the model simulates lawn productivity fairly well. Based on this output, the input appears  175  to be appropriate for simulating maximum production for the study area. It also indicates that the model is reflecting site specific parameters of the study area. The belowground NPP averaged 190 g C m" yr" and simulates a close relationship 2  1  between above- and below- ground production for grasses. The model simulated a much higher root production than the production measured in the field study, at 55g C m" yr" (Table 2  1  3.26). A winter field-sampling program does not likely reflect annual production. The model captures the periods of high as well as the low growth. Thefieldmeasurement in this study occurred during the period of slow growth and low accumulation. 4.5.4.2 Carbon in Storage  2  1  Carbon in storage for lawn, at the end of 20 years, was approximately 5,400 g C m" yr" (Table 4.9). Stubble represented less than 1% of the total C budget. There was no trend in the amount of stubble over time (Appendix C). Variation in the amount of stubble annually was considered to be a response to annual climate influences. The constancy of stubble is similar to the growth pattern of turf grasses and the number of grass plants remains relative constant under a regular management program (Turgeon, 1996; Madison, 1971). The model did underestimate the amount of stubble at 25 g C m", compared to thefieldprogram, which 2  averaged 100 g C m" yr" (Table 3.8). In thefieldprogram, the amount of stubble ranged from 2  1  10 g C m" to 360 g C m". The amount of stubble simulated was in the range found in the field 2  2  study. However, these lower amounts occurred on sites of low productivity where grass plants were frequently replaced with mosses and weeds. The simulation was based on one of the most productive sites in which the stubble was measured at 360 g C m" yr" (Site U3-plot, Table 2  1  3.8). During thefieldprogram a difference in plant cover was noted between the hayfieldsand the lawns. In the hayfieldsspaces occurred between the plants whereas in the lawns, the plants 176  were close together. One characteristic of turf grasses is that the plants produce a dense turf with little space between plants (Madison, 1971; Turgeon, 1996). Consequently, the observed amount of stubble was higher in the lawns than in the hay crops. Therefore, the model algorithms will tend to underestimate the amount of stubble in lawn, as the model has been designed for hay crops. If the amount of 360 g C m" (stubble of Site U3-plot) was substituted 2  into the simulated budget, the stubble would represent 6% of the simulated budget rather than less than one percent. Therefore, the model appears to underestimate C stored in the stubble in lawns. This has the effect of lowering the C budget by approximately 5%. Table 4.9 Simulated carbon budget for lawn. Pool  Storage - after 20 years (average) gCm % of total % change 2  Stubble Roots Soil litter Microbial pool Soil-active pool Soil-slow pool Soil-passive pool (Total soil) T O T A L carbon  25 (25) 150 (150) 85 (85) 10 (10) 140 (170) 2,720 (3,315) 2,285 (2,295) (5,145) (5,780) 5,415 (6,050)  <1  no trend  (<D 3 (2) 2 (1) <1  no trend no trend -33  (<D 3 (3) 50 (55) 42 (38) (95) (96) 100  -28 -33 <-l -22 -21  The model generated surface litter that originates from dead material sloughed from dead grass plants. The litter material was not included in the total budget calculation as this was  177  removed during regular raking. If it had been included in the budget, it would have represented approximately 1% of the total budget. In the simulation, roots represented 3% of the total C budget at 150 g C m". The model 2  generated greater root biomass than measured in thefieldprogram (80 g C m"), a difference of 2  70 g C m" which remained relatively constant (Appendix C) over the 20 years of the 2  simulation. Root dieback is indicated by the model by the fact that even though there was an average root NPP of 190 g C m" yr" (Table 4.8), there was only an average of 150 g C m" in 2  1  2  storage. The surface microbial pool represented less than 1% of the total C budget at approximately 10 g C m". This pool decreased by 33%, over the 20-year simulation. The 2  simulation was initiated with a microbial biomass of 20 g C m" , the minimum set by the model, which represented approximately 33% of that in a grassland system. It was decreased for the lawn simulations as the inputs in the lawn included chemical fertilizers and thus microbial activity was expected to be lower as microbes require organic material to sustain their populations (Brady, 1990). The detailed output (Appendix C) indicated that the microbial pool decreased by approximately 50% in thefirstthree years of the simulation and then remained fairly constant for the remainder of the simulation. This suggests that the simulated lawn productivity could not sustain the microbial pool at the initial level of 20 g C m", and that 2  the initial input was too high for this combination of "crop", site, and management program. This is as expected as the only inputs were chemical fertilizers. Similar to the effects of chemical fertilizer (Table 4.9) in the hay simulation, the use of the chemical fertilizer resulted in a large reduction in the microbial pool suggesting the use of organically based fertilizer on lawns. Further, it suggests that lawn clippings, if left on the lawns, would provide a source of 178  organic material to sustain or increase the microbial populations. The model is designed such that the input into this pool originates from surface litter (Figure 4.7), and lawn clippings would be included in this category. Emmons (1984) noted that it is a good management practice to leave the clippings, if short, on the lawn after mowing to provide a valuable source of nutrients and thus reduce the need for fertilization. •y  By the end of the simulation, soil C was at approximately 5,100 g m" and represents 96% of the total C budget. The results indicate, however, that over time, soil C decreased by 22% over the 20-year simulation. The active pool decreased by 28% or 55 g C m" over this 2  period and this is the only land use where the active soil pool decreased. This pool represented 3%> of the total C budget. Inputs to the active pool are from root dieback (Figure 4.7). The slow soil pool decreased substantially over time, by 33%> or 1,355 g C m" over the 20 years, at 2  approximately 70g C m" yr". Inputs to the slow pool are from the active soil pool, roots, 2  1  surface litter, and the microbial pools. Although there were additions to the soil slow pool, decomposition was occurring (Figure 4.7) which resulted in a net loss of C in this pool. The slow pool represents 55% of the total budget. •y  The passive pool consisted of approximately 2,300 g C m" , which represents 38% of the C budget. This pool remained fairly constant, decreasing by <1%> or 15 g C m" over the 2  20-year period, less than 1% per year. The dominance of the slow pool decreased with time representing an average of 55%> of the budget during the simulations but decreasing to 50%> by the end of the budget. This was expected with the great losses occurring in the slow pool. By default, the dominance of the C budget by the passive soil pool increased.  179  One of the major interests in this study was to assess whether lawns are C sources or sinks. The C assimilated in the aboveground portion is harvested and thus removed from the system. It appears that lawn, when chemically fertilized, is a net source of C. Over the 20-year period, the C budget decreased by 20% or 1,400 g C m", an average of 70 g C m" yr". At 2  2  1  some stage though, it would reach a steady state. The greatest loss occurred in the soil slow pool in which C decreased by 1,355 g m". The increased frequency of cutting results in a 2  decrease in root production (Madison, 1971) and thus soil C, as roots, susceptible to dieback, contribute to the soil pool (Walton, 1983). Further, the use of chemical fertilizers results in less C added to the soils in the form of an organically based fertilizer. Opportunities to increase C sequestration in soils could include the use of organically based fertilizer as can be seen by the effect of using a chemically based fertilizer compared with the manure based fertilizer in the hay simulations (Figure 4.9). Increased cutting height (Madison, 1971), watering during droughty periods to maintain photosynthesis and root growth, abandoning the practice of removing clippings from the surface as a means of providing a substrate for microbial populations, and management to maintain high grass plant density thereby discouraging weed and moss presence, also result in increasing C sequestration. The model indicates that, under the management regime undertaken, lawns are a potential source of C, thereby potentially contributing to global warming and represent an unsustainable land use in terms of net C assimilation. 4.5.5  Comparison of Simulated NPP of Natural Forest, Urban Forest, Hay and Lawn Land Uses  A comparison of the total NPP of all of the land uses indicates that the urban forest assimilated annually the greatest amount of C (Table 4.10), 9%> more C than the natural forest. 180  This has been attributed primarily to the higher soil C inputs, which are related to nitrogen, used to initialize the simulation, as was indicated by the sensitivity analyses. The higher productivity of the urban forest essentially reflects higher soil fertility. Urban forest NPP was 152% greater than lawn although both simulations were initialized with the same site input data. The fact that the higher NPP occurred in the urban forest system indicates the importance of trees in contributing to the C fluxes in an urban setting. They far exceed the potential of lawn to offset global warming. This indicates that choices are available in urban systems that can affect C assimilation. Manured hay production was 74%) of that of the natural forest, and lawn production was 59% of that of hay.  Table 4.10 Summary of simulated carbon budgets averaged over 20 years- NPP and NEP. Pool  Urban Natural Forest Forest gCm'yf' gCm yr' 2  Hay Lawn (manured) gCm yr' gCm'yf 1  2  (percent of total) (percent of total) (percent of total) (percent of total)  Aboveground Coarse roots Fine/small roots (Total roots) TOTAL  550 (63%) 70 (8%) 250 (29%) (320) (37%) 870 (100%) 520  600 (63%) 75 (8%) 270 (29%) (345) (37%) 945 (100%) 405  Decomposition respiration 350 540 NEP** *Values include all years except cultivation years; ** NEP: net ecosystem production.  315* (49%)  185 (49%)  325* (51%) (325)* (51%) 640* (100%) 495*  190 (51%) (190) (51%) 375 (100%) 310  145*  65  In aboveground production, the NPP of natural forest was approximately 75% more than hay, and 200%) greater than lawn. This has implications in terms of C storage as the NPP 181  generated by a tree is transferred into the storage component of the ecosystem. The NPP of the hay and lawn is harvested on an on-going basis such that little of the C assimilated aboveground is transferred to the storage component. This indicates that not only is the NPP of a system important but as well consideration should be given as to whether that C is transferred to storage. A comparison of all three land uses indicates that roots are an important component of total NPP. Roots represent 51% of total hay and lawn NPP, and 37% of forested systems. Trees have a coarse and fine root component while grasses have onlyfineroots. Thefineroots in the manured hay system sequestered annually almost the same amount of carbon as the coarse andfineroots together in natural forest. A review of Table 4.10 indicates thatfineroots of hay sequester annually almost 30%> more C in thefineroot component of trees in an natural forest and 70% more than lawn. Although the aboveground NPP of the natural forest exceeded lawn by 200%),fineroot production of the natural forest exceeded that of lawn by only 32%. This indicates the major contribution offineroots in the NPP of a grass system. Coarse roots represent 8% of total tree NPP. A review of the relationship between the above- and belowground NPP in the hay and lawn systems (Table 4.10) indicates that the model estimates belowground production of grass fine roots directly from aboveground production. It must be remembered that only the aboveground NPP was direct data input and below-ground production, i.e.,fineroot production of the grasses was generated by the model to equal aboveground production of the grasses. The NPP values and ranges of the values under different management scenarios in the hay and lawn systems are important even though the crops are harvested. They indicate the role of land use and management in affecting C assimilation rates. In these systems  182  management can result in an increase/decrease of C assimilation rates and thus they can be managed optimally to maximize C storage. For example, management can result in an increase in belowground production, thereby contributing directly to the long-term soil C storage pool. Root production of hay was 71% greater than lawn production. If these are the only land use options available, hay would be considered the superior use for assimilating and, thus, storing C. This may be consideration for maintaining land in hay production, an agricultural use, rather than converting it, for example, to a golf course. The lower NPP of lawn is attributed to a lower initial soil C content input in the simulation, a lower maximum monthly NPP input, to the use of chemical fertilizers, and to the frequency of cutting of the lawn. This is based on the findings of the field program on the lawn areas. The comparison of the three land uses indicates the importance of not only the aboveground production but to the importance of the belowground production, representing approximately 35%> of the NPP of the forest systems simulated and 50% of the grass systems. In terms of NEP, the model indicates that there is greater NEP under an urban forest than a natural forest. This represents the net amount of C assimilated by a system, taking into consideration the C lost from the system through both autotrophic and heterotrophic respiration. The greater NEP in the urban forest is attributed to a combination of greater NPP in the urban forest as a result of greater soil fertility and less decomposition respiration as there is less substrate available for microbial decomposition (i.e., less CWD available as substrate for decomposition). This is indicated by the higher decomposition respiration of the natural forest, the highest of all of the land uses, compared to that of the urban forest. However, the CWD  183  originating in the urban area is subject to decomposition off-site such that the discrepancy between the natural and urban forest may be less if this is taken into consideration. The NEP of hay crops (during non-cultivation years) is less than half of the natural forest even though the NPP of hay is 75% of that of the natural forest. The decomposition respiration is almost as much as occurs in the natural forest. Less decomposition respiration occurs in the chemically fertilized hay crop (Appendix C) than in the manured hay crop. Therefore, the higher decomposition respiration of the hay crop is attributed primarily to the addition of manure that provides an organic substrate for microbial populations. Lawn had the lowest NEP, being less than half of the hay crop. The NEP does indicate the net amount of C assimilated on an annual basis. This is an important parameter when comparing non-harvested crops or when the actual amount of C assimilated on an annual basis is of interest rather than if it is transferred to long-term storage (measured in decades, for example). With harvested crops, the hay and lawn is removed on an annual basis throughout the growing season. The greater part is re-released back into the atmosphere generally within one or two years. 4.5.6  Comparison of Simulations - Carbon in Storage of Natural Forest, Urban Forest, Hay, and Lawn Land Uses  The average results of the simulations indicate the land use that assimilated the greatest amount of C over time is the natural forest (Table 4.11). The model predicted that the natural forest in the study area would assimilate 5%> more than the urban forest, three times more than the manured hay crop, and five times more than the lawn use.  184  Table 4.11 Summary of simulated carbon budgets - storage (average of 20-years). Pool  Natural Forest  Urban Forest  Cm (trend,%) [% of total/ 8  Aboveground Coarse Woody Debris Surface Litter Coarse Roots Fine/Small Roots Microbial Soil Litter  2  19,245 (+19) r58%l 2,190 (+256) [7%] 360 (-73) r<i%i 3,575 (+17) rn%i 415 (0) n%i 100 (-40) [<1%1 380 (+19)  n%i  Soils  6,680 (+22) [20%]  Hay (manured)  Cm (trend.%) [% oftotaiy 8  2  19,615 (+22) T63%] 320**(+970) [1%]  3,635 (+20) ri2%i 445 (0) [1%1 45 (-18) r<i%i 450 (+128)  n%i  6,735 (+6) [22%]  Hay Lawn (chemically fert.) 8 Cm 8 Cm g Cm (trend,%) (trend,%) (trend,%) [% oftotaiy [% oftotaiy [% oftotaiy 2  2  2  135* (0) [1%1  115*(0) n%i  T<1%1  365(+133)  170 (+65) T2%1  \*%\ 305* (0) 60 (+50)  265* (0) T3%1 30 (+67)  r<i%i 240 (+67)  205 (+20)  T3%1  m 8,620 (-2) [89%]  TOTAL 32,945 (+23) 31,245 (+20) 9,725 (+2) *Values reflect all years except cultivation years; Trend = % change over time; ** Urban coarse woody debris: coarse roots only.  25 (0)  8,300(-10) [91%]  150 (0) \2%] 10 (-33) (<1%) 85 (0) [1%1 5,780 (-22) [96%]  9,085 (-8)  6,050 (-21)  r<i%i  R%1  The aboveground portion of the trees represented approximately 60% of the total forest budget whereas the stubble in the hay and lawn systems represented 1%, or less, of the total C budget. This is as expected as the aboveground biomass produced on an annual basis is harvested in the hay and lawn systems whereas the trees are left on-site for the duration of the simulation. Trees continue to accumulate C, as is indicated by an increase of 19% over the 20-year period, whereas grass stubble did not change from year to year. The aboveground pool in the forest system sequestered 40 times more C than the aboveground pool of the manured hay crop and 625 times more than that of the lawn system. This indicates the importance of choosing a land use for maximizing C storage to offset the greenhouse effect, in which the  185  vegetation has a woody component and is not harvested. This could be interpreted as the best option i.e. to retain forest cover under natural conditions or retain or plant as much tree cover or woody plants in an urban setting as possible. The natural forest stored, over the 20 years, more C than the urban forest. In an urban forest, it is assumed that the aboveground CWD and tree litter are removed during regular yard maintenance. The absence of aboveground CWD and litter represented a budget loss of approximately 2,500 g C m" in the urban forest. The aboveground portion of the tree in the 2  urban forest, even though the simulation was initiated with the same tree inputs as the natural forest, stored approximately 2% more C. This is related primarily to a response to the higher soil C input of the urban simulation, being on the order of 11% higher. Soil is a major driving variable in the CENTURY model (Metherell et al, 1993) and the sensitivity analysis indicated that the average natural forest production was increased by 5%> as a response to the higher soil C, which is directly related to soil N, found in the urban soils. Trees have a major C storage pool that is not present in the hay/grass systems, namely the coarse root pool. A large amount of C is stored in coarse roots, on the order of 3,500 g C m", which represents about 11%> of the total forest budget. This indicates the 2  importance of including the coarse root component in a C budget of a forest system. Coarse roots are net accumulators of C, as indicated by the positive trend in accumulation over the period of simulation. Both the natural forest and manured hay systems sequester C annually in thefineroot component, 29%> of the forest NPP and 51% of the hay NPP. However, the amount of C in storage infineroots shows no trend over time. This indicates no net change in storage for, as noted earlier,fineroots are subject to dieback. As C in this pool is not accumulating over time,  186  fine roots represent only a small part of the C budgets. They represent 1% for the forest system and 3% of the hay C budget. The amount of C in the fine roots of hay doubled that of lawn. This was as expected, as belowground production is closely connected to aboveground production and lawn production was 60% of hay. To achieve maximum storage in terms of roots, a hay crop is a better choice than lawn. The surface microbial pools in all systems generally decreased in the early part of the simulations and then stabilized. It was larger in the natural forest system than the urban forest, by 120%). This difference is attributed to the presence/absence of CWD and litter aboveground providing the major energy source to the microbial pool. In both systems, the microbial pool decreased in the early years of the simulation and then reached a steady state, consistent with the trends in the leaf pool, the pool contributing to the litter pool. The average surface microbial pool in the hay simulation was 40%> less than in the natural forest. In the hay simulation, the microbial pool did increase with time. This was attributed to the surface litter additions from the crop and the manure applications. The chemical fertilizers depressed the surface microbial pool in comparison to the manured hay crop. The microbial pool in the chemically fertilized simulation was half that of the manure based system. The difference in the size of the microbial pool in the manured hay simulations, in comparison to the chemically fertilized crop, indicates the influence of amendments on C storage. As well, it indicates the potential use of the model in making predictions by applying different scenarios. Cultivation did result in a decrease in the surface microbial pool under both hay systems, after which it resumed accumulation. Therefore the microbial population was slightly higher with increasing time into the rotation.  187  The lawn system was generally less productive and had a lower C budget than the hay crops, the microbial pool was less in the lawn than under hay. It was one sixth of that occurring in the manured hay crop and one third of the chemically fertilized hay crop. The soil litter pool was largest in the forest simulations. It was largest in the urban forest, then the natural forest, followed by the manured hay. Soil litter in hay was at 63% of that of the natural forest. Lawn had the least amount of soil litter at 20%> of that of the natural forest, and 35%> of the manured hay. As the lawns were not tilled and as root production was less than that of hay, it was expected that soil litter would be less under lawn than hay. Soil litter ranged from 450 g C m" in the urban forest to 85 g C m" under lawn 2  2  indicating the role of trees in contributing soil litter. Soil litter, however, is not a major C pool. It represents only 1% of the C budget of the forested systems and 1 to 2%> of the grass systems. Soil litter originates from roots and the incorporation of aboveground material during cultivation. The soils are a major C pool under all land uses, representing on the order of 20% of the total budget in the forested systems, 89% in the hay systems, and 96%> of the lawn system. The hay soils under manure management had the highest C content of all land uses. The model predicted that the average value was greater than that of the natural forest soils by 29%> and greater than lawn soils by 49%, over the period of the simulation. Although the natural forest stored 40 times more C aboveground than hay, the total budget of the natural forest was only three times as much as the hay land use. This is the result of the high soil C under hay that narrowed the gap between the two. Are the soils a C sink? In the natural forest system, the soil C pool increased over the period of the simulation, by approximately 20%> or 1 % > per year. This represents approximately  188  1,300 g C m" over the 20 years or 65 g C m" yr" indicating that soils are a C sink. In the 2  2  1  0  1  urban forest, soil C increased only by 6% over the period of the simulation or 20 g C m" yr" , also indicating that it is a C sink. Even though the urban trees had a higher NPP than in a natural forest, the soil C increase was less in the soil under trees in the urban forest. This is attributed to the absence of CWD and surface litter, as well as a lower surface microbial pool. This indicates the importance of these pools in a forested system. Soil C decreased by 2% in the manured hay or by 8g C m" yr". The decrease occurred 2  1  in the early part of the simulation, after which it stabilized. This suggests that the decrease could be transformed into an increase by changing the management as C in the other pools under hay increased with time. Soil C decreased by 10% or 45 g C m" yr" under the 2  1  chemically fertilized hay simulation, indicating it is a C source. It also decreased under lawn, by 22%o or 70 g C m" yr", also indicating it is a C source. 2  1  The model can also be used to predict the effect of amendment choice. The soil in the chemically fertilized hay crop was a greater C source than under a manured system. Soil C in the chemically fertilized simulation decreased by 10%> over the period of the simulation, while only decreasing by 2%> with manure. Thus, a chemically fertilized system resulted in a larger C source, by five times. This indicates the major influences that land use and management can have on soil C. Management can also affect the amount of C assimilated, for example, by increasing the length of the rotation. Hay is also likely a better agricultural use than annual crops, as with annual crops, the soils are regularly cultivated. The effect of trees on soil C is illustrated by comparing the soil C pools under urban forest and that of lawn. In both the urban forest and lawn simulations, the initial soil C inputs were the same. With time, however, the soil C under trees increased by 6% while it decreased  189  by 22% under lawn. This suggests that to maximize soil C storage in an urban setting, tree cover is a better option than lawn cover. This is particularly important in that the soil C under lawn represented 96% of the total budget and thus, the major C pool of this land use was continuously diminishing. Soil C in the lawn system had not yet stabilized by the end of the simulation indicating a non-sustainable system in terms of C. The above discussion indicates that the evaluation of land use in terms of attaining maximum C assimilation should include not only the total budget, but also the trends of C fluxes over time. A review of Table 4.11 indicates that when considering the total budgets over the 20-year simulation, the largest change in C sequestration occurred in the natural forest, increasing by 23%. The urban forest was also a C sink but was less effective than the natural forest, increasing by 20%>. Manured hay is also a land use that is a C sink as C increased by 2%. The chemically fertilized hay crop was a C source with a decrease of 8%>, indicating that management can be carried out to maximize C assimilation. Lawn use was also a source of C, decreasing by 21%, almost double that of chemically fertilized hay. It appears that the best management practice to maximize C storage is a natural forest. This can be attained through several options: 1) retaining the natural forest; 2) where none exists, creating forest plantations (Sedjo, 1989); and 3) converting agricultural land back into forested land (McCarl and Mac Callaway, 1995). Agroforestry plantations, a combination of growing trees and agricultural crops on the same land, have also been considered as a means of countering the greenhouse effect (Kursten and Burschel, 1993). Lawn production is a C source and should be limited if possible in terms of land use. Carbon retention could possibly be increased by lawn by the addition of organic fertilizers. The fact that lawn use is a C source suggests that land uses, such as a golf course, are C sources. Therefore, they aggravate the greenhouse effect.  190  4.6  SUMMARY  AND  CONCLUSIONS  The C E N T U R Y model is a suitable tool to assess the impacts of land use (forest, agriculture [hay], and urban [trees and lawn]), on C storage and assimilation rates. The simulations were initialized with data collected from a field study carried out in Abbotsford, B.C., and from the literature. The natural forest simulation was based on a second growth Douglas-fir forest. The urban forest simulation was based on the natural forest simulation but was adjusted to accommodate an urban ecosystem. The agricultural use was based on a hay crop placed on an 8-year rotation. It was run with a manure amendment and then rerun with a chemical fertilizer in order to assess the impacts of these amendments on C storage and NPP.  The lawn  simulation was based on cutting the lawn monthly and amending it with a chemical fertilizer. Sensitivity analyses were carried out to assess the model's response to various sitespecific inputs. The analyses indicated which inputs had little or no effect on the output. They indicated the confidence of the output based on the inputs used to run the simulations. They were used to narrow the input values when a range of input values could have been used - that is, they can be used to tailor the model to various physical environments. As well, the results of the sensitivity analyses contributed to the final analysis of the simulations. The most sensitive parameters were maximum monthly potential NPP input, amount of annual NPP allocated to a maximum monthly NPP,  soil C content, soil texture, drainage, and soil C:N  ratios. The model was not sensitive to percent respiration of the tree component, the LAI, nor the soil Db. The output included the above-and belowground C storage pools, including the soil, and the NPP.  The NPP is used as a means of calibrating the predicted crop production for different  191  environments, species, and varieties. A comparison was made between the simulations and the C budgets upon which they were based. The comparison indicated that the model simulated well the conditions at Abbotsford. The output was generally within the measured range occurring in the study area. The model did produce a higher fine root component compared to the findings of the field investigation. Fine roots are subject to dieback on an on-going basis and the model simulated this very well. As well, thefineroot biomass varies throughout the year, with periods of high and low growth. The difference between the model andfieldinvestigation is related to the fact that the model simulates root biomass throughout the year, accommodating high and low growth periods and adds them to produce an annual root biomass. In the field investigation, the roots were sampled in the winter, which was the period of lowest root biomass. It follows the period of dieback as a result of the hot, dry conditions of the latter part of the previous growing season but does not include the high root biomass from the spring growth peak. This indicates that several sampling periods should be carried out during the field season in order to assess root biomass. The simulations indicated that the natural forest sequestered the largest amount of C, approximately 5% greater than the urban forest, or three times as much as the manured hay crop, andfivetimes as much as lawn. The higher C storage of the natural forest, in comparison to the urban forest, is attributed to the additional C storage pools of the aboveground CWD and surface litter which was considered to be absent from an urban system. In an urban system, dead twigs and fallen trees are generally removed and litter is removed during regular raking of lawns surrounding trees. The NPP of the urban forest was 9% greater than the natural forest and this was attributed to the higher soil C, and thus, N content of the urban soils.  192  In terms of aboveground C in storage, the natural forest sequestered 40 times more C than the hay and 625 times more than the lawn. Both the hay and lawn are harvested on a regular basis and this indicates the importance of the selection of a land use in which the crop is not harvested and woody in nature. The range of the amount of C sequestered in the forest and manured hay system was narrowed due to the high C content of the hay soils. In the forested system, the soils represented 20% of the total C budget, and in the hay and lawn systems, the soils represented 89%o and 96%>, respectively, indicating the importance of soils as a major C pool. The largest soil C pool occurred under the manured hay system of which the average was 29%> greater than that which occurred in the natural forest, and 49%> more than that which occurred under lawn. Thus, the large C pool under hay narrowed the gap in total C storage between the natural forest and manured hay crop. The CENTURY model separates soil C into active, slow, and passive pools. In all land uses, the largest % > change occurred in the active pool, which is as expected as the turnover time is months to a few years. This pool only represented 3% of the soil C pool and so large changes did not translate into large amounts of C per square metre. Changes were also noted in the slow pool where a small percent change represented a large amount of C as the slow pool represents 65%> of the soil pool. No change occurred in the passive pool in any of the land uses, as expected, as the turnover time is 400 to 2,000 years. In all land uses, except lawn, there was a net increase in the active soil pool. In the forest simulations, there was a net increase in the slow soil pool. However, in the hay and lawn simulations, there was a decrease in the slow pool, the greatest occurring under lawn. The decrease in this pool, because of its dominance, resulted in the soils under hay and grass being  193  a C source. This represents a loss of 2% in the manured hay soils and 22% in the soils under lawn. In terms of selecting a land use to offset the greenhouse effect, it is important not only to develop the C budgets for each system but it is also important to determine if the land use is a source or sink of C. This was carried out by comparing C storage over time. The results suggest that the natural forest is a C sink with a net increase in C of 22%> over the 20-year simulation or the assimilation of 340 g C m" yr". The urban forest was also a major C sink, 2  1  although less effective than the natural forest, increasing by 20%> over the 20-year period or 280 g C m" yr". The manured hay crop was also a C sink increasing by 2% or 10 g C m" yr". 2  1  2  1  The chemically fertilized hay crop was a C source. This C budget decreased by 8%> over the 20-year period at 40 g C m" yr". This simulation was exactly the same as the manured system 2  1  except for the amendments. This illustrates the potential of the model to simulate the effects of different management scenarios, a means of predicting the effect, rather than having to wait until it has occurred. It also indicates that C assimilation can be increased through management. The model simulated lawn use as a major C source as C in storage in this system, decreased in time, by 21%> over the 20-year period, for a loss of approximately 70 g C m" yr" . The amount of C lost could likely be countered to some extent by the use of organically based fertilizers and adopting the practice of leaving the clippings on the surface. However, from this, it is concluded that lawn use is not sustainable in terms of C and the use of lawn aggravates the greenhouse effect. This has major implications in terms of the argument that golf courses are an equal alternative to hay production. It appears that uses such as a golf course may represent a C source and a hay crop, a C sink.  194  The CENTURY model not only has the capacity to show trends of the various parameters with time but as well it has been designed to produce additional output based on the inputs. For example, the model produces output on decomposition (heterotrophic) respiration. Heterotrophic respiration is an important consideration in answering the C source/sink question and in the calculation of NEP. The decomposition respiration of the natural forest, urban forest, and lawn simulations indicated no trends over time but fluctuated over the period of the simulation. The respiration did increase substantially and exceeded the NPP in the hay simulations in the years in which cultivation was carried out. This suggests that the hay crop in those years was a net source of C. This is an important model output. It supports previous findings that agricultural land uses requiring regular cultivation such as annual crops, will be C sources. Further, it suggests that longer periods between cultivation events are desirable. This indicates that the model is a valuable tool in predicting the long-term effects of various land uses on C sequestration. The NEP was greatest for the urban forest, being 54% greater than the natural forest. This is attributed to the high NPP of the urban forest and less decompositon respiration (R ) n  due to the absence of CWD. The NEP of the hay (not including the cultivation years) was 41%> of the of the natural forest system. Lawn has the lowest NEP at 45%> of that of hay. Thus, the forested uses have the highest net accumulation of C.  195  5  DERIVATIVE M O D E L - C L U  5.1 Introduction  To be useful as a planning tool for non-scientists for assessing the impacts of land use on carbon storage and assimilation rates the question arises: What are the key concerns to address in preparing such a model - a model which can perhaps be available in public places such as municipal halls, libraries, or schools, so that the public can carry out "gaming" through demonstrations by community planners and other practicing professionals? It has been recognized that "gaming" can be a means to providing an opportunity to increase awareness of the results of various land use and management options (SCOPE, 1978). One of the major keys to such a successful information system is a friendly user-interface (Fortin and Pierce, 1998). The object is to allow the end-user to utilize the model without having to understand the underlying complexities of the system (Moon et al., 1995). Therefore, one of the considerations in the preparation of this derivative model of CENTURY is to provide it with a user-friendly interface. This was attempted by the development of a derivative model, CLU. How this was accomplished is addressed in this chapter. Another main challenge is to retain the option to input site-specific input to produce site specific output. As CLU is a derivative of the CENTURY model, it has the CENTURY model embedded within it. The integrity of CENTURY is maintained in CLU. This is discussed in this chapter. Other questions arise: How similar is CLU to CENTURY? Is CLU really easier to operate than CENTURY? These can only be answered by comparing the two. Thus, the first part of this chapter describes the steps to produce an output with CENTURY. The second part describes CLU.  196  5.2 CENTURY Model Operation  The CENTURY model is written in FORTRAN and runs in MSDOS. Familiarity in the operation of the model requires a significant time investment and the production of simulations requires a number of steps (Table 5.1). Table 5.1 Steps to operate the C E N T U R Y model. Step  Action  1. Open "File. 100" 2. Create a crop/tree file, based on an existing tree or crop file. 3. Create a weather file (".wth")  3. Create a "Site. 100 file" based on an existing Site. 100 file (e.g. tconif.100) 4. Open "Event. 100" - create a schedule file (".sch") 5. Update "Fix. 100" file 6. Go to M S D O S 7. Run C E N T U R Y  7. Exit DOS 8. Go to Excel  -input selected data -input local climate data in Notepad (max./min monthly temperatures and monthly precipitation, by year) -name as a ".wth file" -input site data - model carries out statistics on weather file -input names of weather and site files; design a treatment schedule, identify crop/tree etc. and treatments for a 12 month period for each year of the simulation or rotation - copy correct fix file to "Fix. 100" a) name a " .lis" file and ".bin" file b) run List. 100 c) name outputs by code [e.g. clittr(l,l)] a) convert to an Excel file b) do required calculations and formatting c) print  The model is divided into several components in which data are input and these files are interconnected (Figure 5.1). The climate data is input in a separate file called a ".wth"file.Site specific data for the trees and crops is carried out in the tree. 100 or crop. 100 files. Data on the site, such as the soil information, are input in a third type offile,the site. 100. Tree data are also entered in the site. 100file,and this must be compatible to the corresponding tree. 100 file as one affects the other. The crop/tree, climate, and sitefilesare all imbedded into the schedule (.sch)file(Figure 5.2), which is developed in a portion of the model called EVENT100.  197  Management events, such as when to plant or what crop to plant are also entered in the schedule file (Figure 5.3). Development of the schedulefilebecause of the input of individual events, is time consuming. For example, the beginning and ending of the growing season and the name of the crop must be entered each year of the simulation as well as all of the activities occurring. The user must "GOTO" the particular month and then insert the action and then go the next year and insert the activities for each year of the simulation or rotation (i.e., a simulation may consist of several rotations each of several years).  EVENT100 Schedule crops and events  CENTURY  .SCH  Soil Organic Matter  file  VIEW  .PLT  Plots a n d Lists  file  Model  C14DATA  <SITE> .WTH  CROP  .100  ''CULT  FERT  FIRE  QRAZ  HARV  .100  .100  .100  .100  100  'IRRI .100  =3 ^MAD  TREE  TREM  .100  ,100  100  erfluifa  FIX  .100  <SITE> I  .100  Irtlgatto*  FILE100 File Manager  Figure 5.1 CENTURY model - programs and file structure (Metherell et al, 1993)  Allfilesare given unique names as they are developed. The user must record separately, usually on paper, the names of the variousfilesand the associated input parameters, as the model keeps no record. Failure to do so will create time consuming searches through the root directory of the model. This requires the opening offilesto identify them and accurate 198  recall of the coding used in the naming of the files. For example, the schedulefilefor hay was named "H0697.sch". As a person learns to the operate the model they may produce several types of thesefilesto asses the impact of changing the various simulations. These may have only slight changes, and therefore, it is important that careful manual records are maintained, dated, and organized. Once all of thefilesare prepared, the model can be run.  L'G MS-DOS Prompt - EVENT100  C:\susan\model2>eventl00 - i h0697.sch CENTURY E v e n t s S c h e d u l e r 01/18/9 4 R e a d i n g from o l d f i l e  h0697.sch...  E n t e r t h e name o f t h e s i t e - s p e c i f i c Old v a l u e : abc3hay.10 0  .100 f i l e :  E n t e r t h e t y p e o f l a b e l i n g t o be done: 0. No l a b e l i n g 1. 14C l a b e l i n g 2. 13C l a b e l i n g ( s t a b l e i s o t o p e ) Old v a l u e : 0  E n t e r Y i f a microcosm i s t o be s i m u l a t e d : Default: N  E n t e r Y i f a C02 e f f e c t i s t o be s i m u l a t e d : Default: N  Figure 5.2 Beginning portion of a schedule file developed in EVENT.100 file in the C E N T U R Y model.  In the model and in the manual, the site-specific parameters are intermixed with the non-site-specific parameters under each type offile(Figure 5.4). The parameters are presented in code, in alphabetical order in the manual, such that each definition of every parameter must  199  be reviewed and the site-specific parameters selected (Figure 5.5). This should be carried out before proceeding with any field program. For example, the soil parameters in CENTURY are based on the upper 20-cm of the soil. Therefore, field studies must be designed to sample at this depth.  HEIE3  p£ MS-DOS Prompt - EVENT100  5.10«20  Ejtic;] %  B l o c k #1  CROP PLTM HARV FRST LAST  m m  Sjfi?  m  '  Year: 1 of 8 Start: Apr Jan Feb Mar  1972  May  End: :L987 Comment HAY MAX PROD BLAIR M Sep Oct Nov Dec Jun Jul Aug  ABHAY X H  H  X X  SENM  FERT CULT OMAD IRR I GRA3 EROD FIRE TREE TREM TFST TLST System  A90 C  commands:  F I L L NEXT NXTA DBLK CBLK TIME C u r r e n t d a t e : J a n u a r y o f Year 1 User command:  GOMT PREV  NXYR DRAW  GOYR DRKFA  CPYR HELP  NBLK SAVE  GBLK A B L ^ QUIT  Figure 5.3 Typical screen in preparing the schedule in the EVENT.100 file in the CENTURY model.  The model is unusual in that the tree and crop options are presented generally under the name of research centres (Figure 5.6). For example, a crop named CPR represents a C3 shortgrass. The name of CPR originated from the scientists who provided the data for the grass. It is named for a research centre formerly called the Central Plains Experimental Range. 200  Similarly, a tree called B N Z Bonanza, represents the boreal forest in Alaska and B N Z stands for the Bonanza Creek Experimental Forest. The manual provides no information on what these names mean or where the data were collected. This information was obtained through a US L T E R Network site on the Internet which was identified through contact by email through those working on the model in Colorado, U.S.A . Therefore, to even run the model the user must first decipher the names of the trees and crops.  V filelOO  Commands: D F H L Q <new v a l u e > < r e t u r n > Name: 'IVAUTO' P r e v i o u s V a l u e : 0.0 Enter response: Commands: D F H L Q <new v a l u e > < r e t u r n > P r e v i o u s V a l u e : 1.00000 Name: 'NELEM' Enter response: Commands: D F H L Q <new v a l u e > <return> P r e v i o u s V a l u e : 49.05 Name: 'SITLAT' Enter response: Commands: D F H L Q <new v a l u e > <return> Name: S I T L N G ' P r e v i o u s V a l u e : 122.32 Enter response: 1  H L Q <new v a l u e > <return> Commands: D F P r e v i o u s V a l u e : 0.31 Name: 'SAND' Enter response: Commands: D F H L Q <new v a l u e > < r e t u r n > Name: 'SILT' P r e v i o u s V a l u e : 0.62 Enter response:  Figure 5.4 Example of an input screen in a Site.100 file in the CENTURY model. Once the inputs are finalized and the site and schedule files are developed the correct "fix" file must be copied to "Fix. 100". For example, i f running a tree simulation, "Tfix.100" must be copied to "Fix. 100".  201  Following the run, the user opens the "LIST. 100" file and names the "bin" and ASCII (.lis)files,and the start- and endpoints of the simulation. The model is then ready to receive the coded outputs selected, for example, wdlcisf 1). which is the code for C in the branch material of coarse woody debris. Any deviation from the parameter spelling e.g., failure to close a bracket aborts the whole run and the created ".bin" and ".lis" (ASCII)filesmust be deleted and the run restarted with each output parameter retyped. This can be fairly time consuming if a typing error is made, for example, on the 15 parameter typed as all previous th  entries are deleted. These kinds of problems are not described in the manual and only become obvious through experience. Finally, the user must close the model and open EXCEL and convert the ASCIIfilesto EXCELfiles,reformat the sheet, and carry out the calculations required. This step is not described in the manual.  202  DEFINITIONS O F C E N T U R Y PARAMETERS  ,  '  V e r s i o n 4.0  decw2  m a x i m u m decomposition rate constant for wood2 (dead large wood) per year before temperature a n d moisture effects applied  decw3  m a x i m u m decomposition rate constant for wood3 (dead coarse root) per year, before temperature a n d moisture effects applied  fcfracCS.l)  C allocation fraction of new production for juvenile forest (time < swold) (1,1) = leaves • ' (2,1) = fine roots (3,1) = fine branches (4,1) = large wood (5,1) = coarse roots  fcfrac(5,2)  C allocation fraction of new production for mature forest (time >= swold) (1,2) = leaves (2,2) = fine roots (3,2) = fine branches (4,2) = large wood (5,2) = coarse roots  leafdr(12)  monthly death rate fractions for leaves for each m o n t h 1-12  btolai  biomass to leaf area i n d e x (LAI) conversion factor for trees  klai  large wood mass (g C/m2) at w h i c h h a l f of theoretical m a x i m u m leaf area (maxlai) is achieved  laitop  parameter determining the relationship between L A I and forest production: L A I effect = 1 - exp(laitop * L A I )  -maxlai maxldr  _ forrtf(3)  theoretical m a x i m u m l e a f area index achieved i n a mature forest m u l t i p l i e r for effect of N a v a i l a b i l i t y on leaf death rates (evergreen forest only); ratio between death rate at u n l i m i t e d vs. severely l i m i t e d N status fraction of E retranslocated from green forest leaves before litterfall (1) = N (2) = P (3)S  sapk  controls the ratio of sapwood to total stem wood, expressed as gC/m2; i t is equal to both the large wood mass (rlwodc) at w h i c h h a l f of large wood is sapwood, a n d the theoretical m a x i m u m sapwood mass achieved i n a mature forest ~~ -  swold  year at w h i c h to switch from juvenile to mature forest C allocation fractions for production  Figure 5.5 Example of input parameters presented in manual of the CENTURY model.  203  ""dfilelOO  Mrf Aj JRN J o r n a d a B l a c k Current option i s : Is t h i s an o p t i o n you w i s h t o change?  KBS K e l l o g g _ Current option i s : Is t h i s an o p t i o n you w i s h t o change?  Current option i s : KNZ Konza t a l l g r a s s Is t h i s an o p t i o n you w i s h t o change?  NEAT Niwot R i d g e Current option i s : Is t h i s an o p t i o n you w i s h t o change?  SEV S e v i l l e t a d e s e r t Current option i s : Is t h i s an o p t i o n you w i s h t o change?  Current option i s : Is t h i s an o p t i o n you  G5 g r a s s mixed 75%warm w i s h t o change?  Figure 5.6 Example of crop options in the File.100 file under " C R O P S " in the C E N T U R Y model.  5.3 CLU Operation  . The CLU model has been designed with a Windows format using Visual Basic 5.0. It requires Windows 95 to run and occupies 2.5 MB of hard disc space. CLU is installed on the computer just as other programs with the option of a separate icon to access the program. Efforts to make the CLU model user-friendly in terms of data input and receiving output with a visually appealing interface have been through the use of the Windows format in combination with the graphics provided by Visual Basic. The graphics are important, as they are illustrative, often being easier to comprehend than a text format. The model has interactive 204  capacity, which allows the user to "play" with both the inputs and the outputs of the model. For example, changes can be made by entering numbers or moving sliders. The graphics correspondingly change, for example, when the soil texture is changed to include a larger proportion of sand, the soil profile graphically depicts the change. Arrow keys are also available to make these changes and the graphics to respond. The underlying premise is that these characteristics in combination are less intimidating to the non-scientist, maintain interest, and are easier to change than in the CENTURY format. Colour has been purposefully used in the graphics to maintain interest. Fortin and Pierce (1998) note that a key to a successful information system is a friendly user interface. As well, many computer programs open to a blank screen. CLU has a welcome screen primarily designed for the public to read when they log onto the computer (Figure 5.7). The font style and size are also designed to be visually appealing. By clicking onto "continue", they can easily move to the next screen. Options to examine and/or change only the site-specific inputs are presented in a standard language rather than code. This eliminates the requirement to review every parameter used in the model. The premise is that the planners or other professionals can run simulations for their regions so that the public can view them, make changes, and see the results immediately.  205  E3  B" CLU  Welcome to CLU (CENTURYLand-Use) This program has been designed to allow you to predict the effects that changing a land-use can have on the amount of carbon stored in the vegetation and in the soils and the amount of carbon assimilated (NPP) each year. You may want to know how much less carbon will be stored in the vegetation if you change an areafromforestto hay land or to an urban use. You may want to know what will be the impact on carbon storage if you include parkland into a subdivision plan. The output is on a metre squared (m ) basis making it suitable at the lot level, and this can be scaled upformore regionaltypeofplanning. You can choose to view the output of existing simulations and the inputs used for them and you can take an existing simulation and change it to suit your site. For either purpose, select a simulation that has been carried out close to your site and if that does not exist, choose a land use in an ecoregion similar to your site e.g. if you are living in a temperate region and want to simulate a hay crop, select hay in a temperate region and input your own climate, soils data, etc. to make it more site specific. Good hick! 1  I  Continue ....  Figure 5.7 "Welcome" screen in CLU. CLU has been set up such that the wide range of outputs can be selected. The model can instantaneously present it graphically with summary information and labeling automatically provided. There is an alternative choice to view the output in a pre-formatted, labeled, tabular form. The model has been designed so that these can be printed for later examination. This would be useful when running a number of simulations to compare them. Such graphical and tabular output will be useful in a number of areas such as in presentations to the public, in official community plans, and in planning projects that bridge more than one region.  206  Only four screens are presented: the welcome screen (Figure 5.7), the profile screen (Figure 5.8), the schedule screen (Figure 5.9), and the output variable list screen (Figure 5.10). All steps for the CENTURY model (Table 5.1) are carried out in CLU from the profile and schedule screens. These include entering the data, the rotation length, and the period of the  simulation, selecting the outputs, running the model, obtaining output either graphically or in tabular form, and saving the simulations. The user can easily switch back and forth between screens by clicking on the boxes at the bottom of each screen. The "run", "save", and "exit' commands are also located here.  B" a u fife  Windows Help  Site setup  Soil Profile  # Topsoil layers: |3  Location: BC (hay)  Total* layers: |3  49.05° N, 122.32°W  Coordinates:  Biome: Mesic/subhurrad grasslanc d Initial tree: j (none)  0%  Clay (%): p Silt (%): p 2 ~  d  Initial crop: Hay 75% cool  Sand (%): |31~~  Weather  Drainage (%): \90.0  f~ Rewind file at end of each block  pH: |6.00  Tree organic matter Live {% total): Leaves: JoTi  Dead (kg/ha): Branches: flii  Branches' |u"o  Stemwo o d:lu"o  Stemwood: jo"o  Roots: |n.O  Coarse roots: ji Fine roots: |u.0  Bulk density (kg/L): |0.91 Soil carbon (g/m* C):]8550.20  Maximum mmihip production Forest NPP (g C/m /mo). 10 2  Forest GPP (g biomass/m /mo) 2  Maximum LAI: Cu  Save  Crop NPP (g biomass/m/mo) 2  Next>  Exit  Figure 5.8 A "profile" screen in CLU. 207  B" CLU CAsusarACl.U\hay clu Fie Endows Help Management Import Schedule  schedule 1987  1972  Block 1:1972 - 1987Ootput starting ytax: 1973  Treatment— r Crop r Cultivation V Erosion F Fertilization r Fire Fertffiyitkm  C C C C  I P" I r~  Crop-OS start Grazing Harvest Irrigation  r I" |7 I"  (* C r r  Quality-very high Quaity-max LowN MedN  r ModhighN T HighN r LowP r MedP  -  -  -  Crop-GS end Organic Plant Senescence  I r I I  -  -  -  Tree-GS start Tree-GS end Tree Tree removal  options  Quahty-rnin Qua3ity-med Quality-mod high Quality-high < Back  Save  Run  Next>  C M o d low P r ModhighP r Very high P  i|  Exit  |  Figure 5.9 A "schedule" screen in CLU.  The screens have a typical windows tool bar on the top (Figures 5.11 a-c). For example, a new simulation ("new") or an existing one ("open") can be selected under "File", as well as "save" and "save as", etc. Under "windows" any screen, e.g., profile, schedule, can be selected by clicking on it, including output screens such as graphs or data tables. Under "Help" the user is provided with a complete description of how to run the model (Appendix D) and the option to input a new weather file.  208  ET C L U  C \susan\CLU\2ndDF clu  File Windows  Help  Used output variables  Unused output variables  Total NPP NPP leaves NPP branches NPP wood NPP coarse roots Storage leaves Storage branches Storage wood Storage coarse roots Storage branch CWD Storage wood CWD Storage coarse roots CWD Surface Utter Soil litter Surface microbial pool Soil carbon Decomposition respiration NPP tree fine roots Storage tree fine roots  < Back  Save  Run  Neit>  Exit  Figure 5.10 List of output variables (forest).  gtfMom  Hot)  Hew CUM) Qpen. CM*0 gave CWS. Save A*.. PA i n Satyp.  3 pyM, 122.32° W pubhmmd grasslanc * \  SettPnfUe  # Topsoil layers: |3 Total* layers: p"  0%  Clay <y»): f? s i t <y«)  % cool  ~3  Sand(%}•  "3 r Rew n id fie at end of each btn*  Weather I ATOSford  Drainage t>4): JxTo pH:|o"bo  Tree organic iruOterLive (% total): Leaves: Jo 0  Branches:  Branches: |o o  Stemwood:  Stemwood: Jo o  Soil carbon (gftn» C): |8550.20 Atata  Roots: fi  Coarse roots: |o D Fine roots  Bulk density (kg/L): josi  Dead (kg/ha):  Forest GPP (g biomass/irf/mo): JTo  Maximum LAI.fTi  Sara  toarik^avnAa Forest NPP (g Cfar'/hio):Joo  Crop NPP (gtoomass/mVmo):|520 0 Rim  Next>  Exit  Figure 5.11a Options under "File" in the tool bar in C L U .  209  g * Bofito SchedJe flUptf C firaph Table  Fl F2 F3 F4 F5  SoUPrafUg  fcr  # Topsail layers: |3  "3  Total # layers: p"  9.05° N, U 2 . 3 2 ° W  Clay (%)  Initial tree: | (none) Initial crop: | Hay 7 5 % cool  d  Silt (%)  d  Sand (%)  Weamer:| Abbotsford  d V Rewind Ha al end of each block  (•/<,): POO pttJoO)  Tree organic matterDead (kg/ka).  Uv0 {% total): Leaves: Jo 0  Branches..] 0  Branches: |oT  Stemwood [on  Stemwood: (J O  RoafeJTci  Bulk density (kg/L,): p P l Soi carbon (g/m* C): |8S50.20  Maxbmim monthly production— Forest N P P ( g C/trf/mo) f  Coarse roots: (in  Forest G P P ( g biomass/m /mo): f Crop NPP (g bionrassto7mo): pDX 2  Maximum L A I : lu n  Fine roots: I.• 0  Save  Run  Nezt>  Exit  Figure 5.11b Options under "Windows" in the tool bar in C L U .  FJe  \::us.in\r.l H \ h . » » i:lu |  Windows  Site setup CLUHefc... Locatio.  £dt wsathar definitiora  Coordinates:  # Topsail layers: |3 Total # layers: p  49.05° N, 122.32° W I Mesic/suhbjumid grasslanc  Clay (%) d  sat (%)  d  Sand (Vo)  d l~ Rewind i e at endaf each Uock  Drainage (%):  | (none) | Hay 7 5 % cool  Pi  1 Abbotsford  |6.00  Tree organic matterlive (% total):  Dead (kg/ha).  Leaves. |o"c  Branches: Jo"o  Branches:}:: o  Stemwood: |o 0  Stemwo o i: jo 0  Roc  Forest N P P ( g C/mVmo).foo  Coarse roots: |o o Fine roots  Bulk density (kg/L): JO 91 Soi carbon (gfrn* C):J8550.20  Forest G P P ( g biomass/m /fno):|j o J  Maximum L A I Save  Crop NPP (g toomass^/mo):|520.0  Run  Next>  Exit  Figure 5.11c Options under "Help" in the tool bar in C L U .  5.3.1  Inputs  In the profile screen (Figure 5.8), the user specifies the location, tree and or crop, the weather file, and other site parameters such as the soil information. The existing data can be accepted or changed as required. In the schedule screen (Figure 5.9), the period of the simulation is set as well as the rotation lengths and treatments, etc. The model is designed to access the default data sets through the crop/tree and locations menu in the profile window such that the ".100" files no longer have to be opened. The ecoregions are connected to specific crops and trees files, for example, the NPP of a certain tree is a direct response to particular site conditions. The appropriate "fix" file is automatically included with the tree or crop selected, such that it no longer has to be copied as a separate step, as is done in CENTURY. The model has been designed such that when an ecoregion is selected, the latitude and longitude are also provided. These ecoregions are located on Figure 5.12 (based on the LTER network site map). The latitudes and longitudes are provided in the CENTURY model, however, are not connected by name. For example, the model provides a site. 100 file named tdecid.100. The tree named for this data set is CWT Coweeta. The corresponding schedule file named "tdecid.sch" describes the schedule for a temperate deciduous site type. The tree is named after the Coweeta Hydrologic Laboratory that is located in North Carolina. The latitude and longitude of the sitefile"tdecid.100" corresponds to the Coweeta Site. Therefore, under tree, the CWT tree has been renamed "hardwood", and the site. 100 file of tdecid.100 has been renamed North Carolina (temperate deciduous). The user can now select a hardwood tree and the ecoregion type of temperate deciduous, as a deciduous tree is a hardwood.  211  1. H.J. Andrews Experimental Station 2. Arctic Tundra 3. Abbotsford, BC 4. Bonanza Creek Experimental Forest 5. Coweeta Hydrologic 6. Jornada Experimental Range 7. W.K. Kellogg 8. Konza Prairie Research Natural Area 9. Luquillo Experimental Forest 10. Niwot Ridge/Green Lake Valley 11. Sevilleta National Wildlife Refuge 12. Shortgrass Steppe  Figure 5.12 Locations of ecoregions. Other input options have also been renamed in CLU. For example, in the scheduling file in the CENTURY model, the code for the use of organically based fertilizer is "OMAD". This has been changed in CLU to "organic amend.". A list of the original codes found in the CENTURY model and the corresponding new names in CLU, are included in Appendix D. The list of all of the inputs which are available on the profile and schedule screens are presented in Tables 5.2 (a-e).  212  Table 5.2a Tree/Crop/Fix Options (drop down menus on "Profile" window). Trees  Fix files  Crops  mixed coniferous 2nd D P urban 2nd D P boreal western pine, urban western pine 50/50 conifer/deciduous, urban conifer/deciduous hardwood, urban hardwood  Alpine tundra grass tallgrass C4 grass C4 prairie shortgrass C3 grass 75% warm  arctic tundra dry forest forest tropical boreal forest  grass 75% cool  dry grassland  hay 75% cool  mesic/subhumid grassland  droughty deciduous, urban droughty deciduous broadleaf evergreen, urban broadleaf evergreen tropical evergreen mesquite * Df=Douglas-fir  lawn 75% cool desert grass tropical grass grains  Table 5.2b Location options (drop down menu on "Profile" window). Location (ecotype) New Mexico (arid shrubland, C4 grass, mesquite) Colorado (alpine grass) Kansas (savanna, C4 tallgrass, hardwood) Colorado (prairie shortgrass C3) British Columbia (hay) British Columbia (lawn) Kansas (tallgrass C4)  Oregon (temperate coniferous) Oregon (urban temperate coniferous) British Columbia (2nd Douglas-fir) British Columbia (urban 2nd Douglas-fir) Alaska (boreal) North Carolina (temperate deciduous.) North Carolina (urban temperate deciduous.) Puerto Rico (tropical rainforest)  Table 5.3c Soil/Other Inputs (on "Profile" window). % sand  forest max. monthly NPP (above- & belowground) (g C m /month) forest max. monthly GPP (above- & belowground) (g biomass m /month) crop max. monthly NPP (aboveground) (g biomass m /month) 2  % silt  2  % clay  2  bulk density (kg 1"') PH soil organic matter (g C m" ) % drainage  213  Table 5.2d Management/ Scheduling Options (on "Schedule" window). Management  Scheduling  fertilize organic amendment  C G S - c r o p G S * start  irrigate  TGS - tree G S * start  graze  TGS - tree G S * end  cultivate  Plant  erosion  senescence  C G S - c r o p G S * end  tree loss fire type G S * - crop growing season  Table 5.2e Options for each management action (drop down menus on "Schedule" window). Type cultivation  fertilize  Type irrigate  Definition Plow  to 50% A W S C *  Sweep Cultivate  none to 25% A W S C *  row cultivate rodweed drill  to 15% A W S C *  no-till drill  to 75% A W S C * to 95% A W S C * to 25% A W S C * + 1 0 cm  herbicide  apply 5 cm  maintenance prod, (no nutrient stress) 90% max. production.  apply 10 cm apply 15 cm  grazing  80%o max. production.  moderate  75% max. production.  none  quality-max (maximum nutrient content)  optimal  quality-medium (medium nutrient content)  corn silage winter  high N (5g Nm" ) 2  moderately high N (4.5g Nm" )  low  medium N (3g Nm" )  high  2  2  erosion fire  lowN(lgNm" ) 2  low P* (125kg ha" ) 1  kg m" per month soil loss cold  moderately low P* (188kg ha" )  no fire  medium P* (250kg ha" )  medium  1  1  moderately high P* (376kg ha" )  hot  1  wet  very high P* (564kg ha' ) 1  organic additions harvest  Definition  manure/straw wheat straw  tree removal  clear-cut low growth burn  grain + 50% straw  canopy + C W D * burn  grain  blowdown  roots  no tree removal  hay  canopy burn  thin/remove *N=nitrogen; P=phosphorus as superphosphate; AWSC=available water storage capacity; CWD=coarse woody debris.  2  The inputs and outputs of existing runs can be reviewed, changes made, and new output generated. These may be useful as well, if one wishes to compare the C budget of their area to another ecoregion type. A new simulation can be initiated by selecting the tree/crop and ecoregion. A schedule file can be designed or one that is included in the model can be imported into the new simulation. For example, a hay schedule file has been designed and is named "hay.sch". This was used to run the hay simulation in the CENTURY model. By selecting it, the length of the simulation and the various management options used in the simulation can be adopted or changes made to it. For example, in the "hay.sch" file, manure is applied every June, except in the reseeding years. If one wants to assess the impact of applying manure in August, as well, this can be easily added to the " .sch" file directly on the schedule screen.  Thus, any year of a rotation and any month can be selected and the associated  management options will be displayed automatically in the lower half of the window (Figure 5.9). Also, the list of fertilizer types and applications rates appear on the screen if the file indicates a chemical fertilizer has been applied. The one selected will be marked (Figure 5.9). The schedule can be customized by clicking onto specific years and months. All options are available on the screen. CLU automatically produces a summary table of the inputs used in a simulation (Figure 5.13), facilitating record keeping. This is accessed under the "Window " box in the tool bar as a "Table". CLU has been specifically designed to have space available to the right of the inputs for the user to add the reference or a note. This is a method of record keeping not available in CENTURY.  215  The list of potential output parameters, (Figure 5.10), are automatically specific to the use. For example, there is no coarse root output variable available when running a grass simulation. The output parameters can be selected by using the arrow keys. Once all the inputs have been selected, the user saves this and then selects "Run" from the bottom of the screen.  216  C:\susan\CLU\hay.clu — Thursday, Jul 291999 Profile Site setup  Notes  Location. B C (hay) Coordinates: 49.05° N , 122.32° W Biome: Mesic/subhumid grassland Initial tree: (none) Initial crop: Hay 75% cool Weather: Abbotsford Tree organic matter Live... Leaves: n/a Branches: n/a Stemwood: n/a Coarse roots: n/a Fine roots: n/a Dead... Branches: n/a Stemwood: n/a Roots: n/a Other... Maximum LAI: 0.0 Soil profile # Topsoil layers: 3 Total # layers: 3 Clay: 7 % Silt: 62 % Sand: 3 1 % Drainage: 90.0% Baseflow: 90.0% Stormflow: 0.0% pH: 6.00 Bulk density: 0.91 g/m Organic matter: 8550.20 g C/m Maximum monthly production Forest NPP: n/a Forest GPP: n/a Crop NPP: 520.0 g biomass/nrVmo Schedule Starting year: 1972 End year: 1987 # Years in output: 15 # Management blocks: 1 2  Figure 5.13 Example of summary table of inputs for a run automatically generated in CLU.  217  5.3.2  Outputs Following a run, the form of the output can be selected under the "Windows" box.  CLU automatically generates the output graphically (Figure 5.14). This provides not only the annual output for the length of the simulation, but also automatically provides the average value at the bottom of the graph and presents the average as a line on the graph. The title of the graph, the units, and the year on the x-axis are automatically printed on the graph. This graph can also be displayed with the list of output parameters (Figure 5.15). Each graph can be quickly reviewed on this screen by scrolling up and down the list of variables or using the arrow keys at the top of the screen (Figure 5.16). A small box, "Print this page" allows the user to print selected graphs. Graphs selected for printing are identified by an arrow in the list of output variables. Once selected, these can be printed using the print command once under "File" in the toolbox (Figure 5.17). If the "Hide list" box is clicked, the output variable list is hidden and the graphs appear as in Figure 5.14. The scale in a graph can also be adjusted by clicking on the "scale" box located in the lower left corner of the screen (Figure5.16). The y-axis scale is automatically adjusted to accommodate the amount of variation in the output no matter how small. The scale adjustment can be used in such cases to illustrate long-term trends where the small variation basically reflects yearly scatter.  218  C:\susan\CLU\hay.clu — Friday, Jul 30 1999  Stubble (g C/m ) 2  1974  1976  Average = 140.28 gC/m  !  1978  1980  1982  1984  Year  NPP crop fine roots (g C/m /yr) 2  1974  1976  Average = 316.12 g C/rrrVyr  Figure 5.14 Typical view of graphical output generated in CLU.  1986  B" CLU CAsusan\CLU\hdV clu Soil carbon (g C/mtyrj Output variables Total carbon storage (crop) Stubble Total NPP NPP aboveground NPP cropfineroots Surface litter Soil litter Surface microbial pool Decomposition respiration  -i  1974  r 1976  1978  1980  1982  Scale | Avaraga - 8598.58 g OnrVpT  < Back  1986  Year  Save  Print this page f™  Next>  Exit  Figure 5.15 Graph and output variables.  The user can also obtain the output as a histogram (Figure 5.18). The regular graph is easily converted to a histogram just by clicking on it on the screen. The output can be viewed as a table (Figure 5.19) by selecting "Table" under "Windows" on the tool bar.  220  CLU  B"  Fie  i  \-.u- . i r , \ l I l l V h i v < l u  WJndo«vs Help  <=i| O |  HkteLwt  S a i i cation (g C/mtyr)  |  Output variables  8794  Total carbon storage (crop) Stubble Total NPP NPP aboveground NPP crop fine roots Surface litter Soil litter Surface microbial pool Decomposition respiration  1  1  1  1974  1976  1  1  1978  1  1  1980  1  1982  r  r  T  1984 1986 Year  ScdfeJ Av*rage = 8598.58 gOnfyr  Next>  Run  < Back  Print this page [~  Exit  Figure 5.16 Output with scale adjustment.  |  WMow Heap Mew Qpen... CJose  CtritN CM*0  S o i l carbon  (g  C/mtfyr}  Output variables Total carton ftoxac* (crop) Srtbbk Total KPP NPP abo^coimd KPP asp fiat rootj Surface btt.i SoflKttar Surface inicxoltialp  1974  1976  Scale | Awvg. < Back  1978  -S598.58gOn^  1980  1983  1984 1986  Y**r  Pintunpaga P Next>  Exit  Figure 5.17 Commands under "File" tool box on graph/output variable screen.  Stubble (g C/m ) 2  1974  1976  Average = 140.28 g C/m  1978  1980  1982  1984  1986  Year  2  NPP cropfineroots (g C/m /yr) 2  1974  1976  Average = 316.12 gC/m /yr 2  1978  1980  1982  1984  1986  Year  Figure 5.18 Typical view of a histogram option generated by C L U .  222  C: \susan\tT U \hay clu.  Fie Windows Help SuiuZnary  Total carbon storage (crop) (g  Year  Stubble (g C/m')  C/m*)  Total N P P (g  NPP abovegrouTid  Cfnftyi)  (gCftn*^)  NPP crop fine roots (gCfnffyi)  1972  9214.00  50.00  0.00  0.00  0.00  1974  9333.28  107.64  597.24  290.03  307.21  1975  9447.90  109.86  543.20  268.00  275.19  1976  9550.42  118.95  609.99  302.83  307.16  1977  9652.67  167.09  714.68  350.89  363.79  1978  9755.32  160.85  709.15  347.38  361.77 342.57  1979  9793.31  159.13  668.58  326.01  1980  9839.85  148.91  614.19  299.57  314.61 183.02  1981  9488.92  131.01  518.82  179.27  1982  9716.10  172.81  766.03  380.78  385.25  1983  9774.50  140.58  59237  293.61  298.76  1984  9787.78  145.21  684.87  339.61  345.26  1985  9853.66  135.73  646.20  322.06  324.14  j j _ n  < Back  Save  Run  Exit  Figure 5.19 Typical view of data table automatically generated in C L U .  5.3.3  Data security and integrity  It is important that a model that is to be used by the general public has security associated with it such that the user cannot unknowingly delete critical files required to run the model. Fortin and Pierce (1998) note that this is a very important issue in such a model. Moon et al. (1995) suggest that one function of the user interface of a model is to protect the system from inadvertent damage by unskilled users, that is, "to manage the users". This is not provided in the CENTURY model. A new user of the CENTURY model can easily change a parameter in the original default data sets, thereby losing the value supplied by the model, or delete crucialfilesrequired to run the model. This is of particular concern. Therefore, a  223  system of data security has been provided in CLU to all default data sets and schedule files in order to insure that the user does not lose the original data provided. If thesefilesare opened and changes are made, they will be required to be saved under a new name. Fortin and Pierce (1998) also note that the model should have data integrity - that is, limits on the range of the input data such that the user will be notified, either through the termination of the model run or through extreme data, if the inputs are outside an acceptable range. For example, for the parameter of pH, the model should not accept a pH greater than 14. The CENTURY model has data integrity built in, in some areas, but not in all, such as pH. The CENTURY model will still run if the pH input is at 55. The CENTURY model has built-in data integrity in a set offilesthat arefixedin which there is no option for user input. Also, data integrity is provide for some options through a preset data range, for example, in the DRAIN parameter, which can accept values only between 0 and 1. The CENTURY model supplies data integrity in the management options, for example in the fertilizer selection with each option available at a set application rate. It also supplies data integrity in the area of the plant production models in that plant production cannot exceed that permitted by the site conditions. For example, production is limited by site factors such as moisture, temperature, and insufficient nutrient supplies, with the model invoking Liebig's Law of the Minimum (Metherell etal, 1993). Effort has been applied, to add the additional data integrity in CLU in several areas, for example, in the parameter of pH, in the soil textural components which are based on percentage, and the relationship of the tree components which are also based on a percentage. Integrity is automatic on all of the other parameters based on percentage as well as in the  224  management options, in which the user clicks on their choice. Other than the site-specific inputs, thefileswill be write-protected (secured), including the base default data sets. 5.4  Summary and Conclusions  CLU is based on the CENTURY model and it has been designed to be used by nonscientists for planning, educational, and decision making purposes - as a scientific means to assessing the potential impacts of land use decisions on C storage and assimilation rates. The goal has been to develop a tool that could be used by community planners and other practicing professionals who could demonstrate it to the public in places such as libraries, municipal halls, and schools and provide the opportunity for "gaming". From this, the planners and public may develop an awareness of the impacts of land use changes in their area from a scientific perspective. The effort towards user friendliness has been made in CLU by using colours and graphics. The model is provided with protection such that the user does not unknowingly lose critical data to run the model. The model been supplied with data integrity to eliminate, as much as can be conceived, input data errors beyond an acceptable range. CLU has been designed to eliminate the numerous steps required to run the CENTURY model but retains the CENTURY model within it allowing the user to input site-specific data in order to generate site-specific output. CLU has been supplied with all of the default data sets of the CENTURY model, which include a wide range of ecoregions and tree and crop types as well as a wide range of management options. The names of the parameters and trees, crops, and sitefileshave been renamed for easy identification. CLU automatically provides output graphically (including histograms) and in tabular form. These are labeled automatically and include extra data such as the average values plotted 225  on the graphs. This allows for an assessment of how a particular year deviates from the average. Such graphs can be used in presentations to the public, in the planning process at the local level, and in planning between regions, and amongst municipalities. The model provides an easy means of record keeping by automatically generating a summary of the inputs used in the simulation that can be printed if desired or remain on file with the simulation. All simulations can be retained. Example simulations from all regions can be easily generated by the model, which can be examinedfirstin the program and compared to the study site in question. CLU has been designed to develop an awareness of the potential effects of land use changes in terms of C storage and assimilation rates. At the same time, this model may influence development at the residential lot level, for example, with owners considering the effects of tree removal by providing a C value of trees. This can also be used to support local tree bylaws and future C tax laws or for the bylaws regulating the amount of a land covered with impermeable material. As CLU has the CENTURY model imbedded in it, it can also be used in research being much easier to operate than CENTURY.  226  6  SUMMARY AND CONCLUSIONS  One of the major objectives of this thesis was to compare the effects of forest, agriculture, and urban land uses on C storage and assimilation rates. Carbon budgets have been developed for forest and agricultural systems but there is a paucity of information on the urban system. A review of the literature indicates that previously prepared C budgets are difficult to compare even for those developed for the same land use or among land use as there is no standard set of parameters to include in the budgets and thus they are incomplete. It was also found that there is also no standard methodology, thus the results are not comparable. Studies regarding individual land use effects also have occurred in different biophysical regions and thus comparisons among studies by default, include non-land use effects. To conduct a comparative study, afieldprogram was required in which all three land uses occurred in the same biophysical region, the study was carried out in the same time period, with data collected on the same parameters across all systems using the same methodology. This was required as a means to reducing non-land use influences and experimental error. The study area was located in Abbotsford, B.C. Thefieldinvestigation was carried out on three unmanaged forest experimental units, which were dominated by second growth Douglas-fir trees, four well-established representative hay production units, and ten residential lots managed separately, and an urban park located in a 25-year-old subdivision. The study was carried out over one growing season, 1996. Urban systems include the major components of trees, lawns, shrubs, gardens, and soils. In anyfieldprogram, there is inherent variability as a consequence of both natural heterogeneity and management practices. The urban land use posed the most serious challenges for a number of reasons including a lack of data on urban areas, large spatial  227  heterogeneity of plant cover, the age structure of vegetation, and species numbers. These are the result of a deliberate selection of species and spatial distribution. As a way of approaching this reality, the urban site was considered to be a composite of an agricultural component (hay as a proxy for grass), while rhododendrons, geraniums, and Douglas-fir trees were selected as representations of woody shrubs, annuals, and trees, to allow comparison to forests. Therefore, the urban area, with trees, lawn, and shrubs was approached as representing a hybrid between the forest and agricultural systems. The C budgets indicate that the second growth Douglas-fir trees under natural conditions in the Abbotsford area store the highest amount of C of the three land uses. They store 5% more than an urban forest, three times as much C as a hay crop, four times as much as lawn, and 40% more C than rhododendrons. When the NPP is summed over the ages of the rhododendrons, it far exceeds the estimated carbon in storage. Therefore, this data should be interpreted with caution. Annual gardens have low C budgets as a result of whole plant removal. The soil C pool is greatly affected by land use and soil represents a major C pool across all land uses, the largest amount found in the study area under hay. Soil represents 20% of the forest C budget, 45% of the rhododendron budget, and greater than 95% of the hay and lawn uses. As the soils represent greater than 96% of the C budget in the hay and lawn areas, research on hay and lawn should focus on the soils for C dynamic studies. These findings indicate the importance of soils in terms of storing C and that land use does have an effect on C storage in soil. This provides a solid argument to preserve soils and to consider them as a major C storage pool. In the natural forest system, the soils and aboveground portion of the tree represents 82% of the C budget, suggesting that the focus should be on these compartments in terms of  228  attempts at C management. The aboveground portion of the trees represents 62% of the total forest C budget indicating that efforts should be made to retain the trees as a means of maximizing C storage. If forest use cannot be applied over large areas, trees can be included as pockets within other uses. Fine roots across all land uses, except rhododendrons, represented about one per cent of the total budget. This suggests that, with limited resources for study and implementation of management scenarios, less focus can be put on this component. Litter was sampled in the forested sites. On the site totally occupied by coniferous trees, little difference in litter biomass occurred between the fall and spring sampling periods. On sites, which included deciduous trees, 50% less litter occurred in the fall sampling period compared with the spring measurement. Therefore, the sampling period is important on sites that include a deciduous component. The period selected should be consistent with the goal of the research. If the sampling period is not flexible, the researcher should recognize the implication of the effects of various sampling periods on the amount of litter collected. The forest litter pool represented approximately 3% of the total C budget. If sampling cannot be carried out, it may be estimated as it represents a small component of the budget. This may not hold true for all biophysical regions. Carbon is routinely accepted as 50% of biomass. However, based on C analysis, C content varied widely (41-50%), indicating that assumptions should not be made on this parameter. Carbon analysis should be carried out for each type of material collected. As an agricultural use, hay likely has a higher C budget than other agricultural crops such as vegetables or row crops. Vegetable production, where the whole plant is removed, can be equated to the budget prepared for annuals in which the main C pool is soil. In terms of hops  229  and raspberry types of crops, the soils are predominantly bare between the stocks. For these types of crops, soils are likely the major C pool. Land in hay or pasture use likely represent the agricultural uses to select for maximizing C storage. Other than perennial grass types of crops, the soils in other agricultural operations are routinely cultivated causing a reduction in soil C. The hay crops sequestered annually 75% as much C aboveground as a Douglas-fir tree. This has little impact on C storage, however, as the hay is harvested and is non-woody. Therefore, the assimilated C is not transferred into long-term storage. The hay crop can be considered a short-term sink. A range of C assimilation and storage occurred among sites under hay and under lawn land uses. The variation among sites indicated C storage and assimilation can be increased through management. Therefore, it is important to select the appropriate vegetative cover but, just as importantly to manage it for maximum C assimilation and storage. The compartmentalization of the C budgets is a good tool to guide the researcher to the major and minor storage pools and to illustrate where research should be focused. As well, it indicates where deficiencies may have a major impact on the results of the C budgeting and where data gaps will have little impact. The comparisons indicate that the land use adopted can effect C storage and assimilation rates. As well, they indicate the amount of variability that can occur in a particular C pool. Although these findings are based on Abbotsford, B.C., the trends should be similar in other temperate regions. The focus on the important pools can permit a good estimation of C budgets most efficiently, allowing comparisons to be made between different regions and uses. To maximize C in storage in the Abbotsford area, the land use selected would be forest cover. If this is not an option, mixing covers, including using shrubs, which are the next major  230  C accumulator, can be another option. As land is urbanized, portions are covered with impermeable surfaces and therefore the amount of land available in an urban environment for C sequestration from the atmosphere and transferred into storage in the vegetation and the soil, is controlled by the amount of impermeable surface. Ideally, if the goal is to maximize C sequestration in order to have an impact on the greenhouse effect, then planning decisions, at all levels from the homeowner to regions, and larger, should be to minimize the amount of land covered with impermeable surfaces. Once that decision is made, the vegetative cover can be selected for its potential to assimilate and store C. Another objective of this dissertation was to select an appropriate scientific model to be used to compare the effects of local land use on C storage and assimilation rates. A model allows users from different regions to simulate land use change effects over time and therefore it can be a useful planning tool. The CENTURY model was selected as it could meet the specific criteria required for such a use. CENTURY is a biophysical process model that can accommodate forest and agricultural uses and includes a soil component. As it is developed from a systems approach, the various components are affected through interactions among the pools and driving variables. The model is well documented in the literature and has been applied to agricultural and forested ecosystems but it has not been used to simulate urban land use C dynamics or for forest vs. agricultural comparisons for C management purposes. The output data of the CENTURY model is particularly suitable for the urban land use since the calculations are done on a square metre. In using the CENTURY model, urban land use was accommodated again by considering it a mosaic of forested and agricultural land uses, with the trees in the forest representing trees left on residential lots, and hay as a surrogate for lawn. The inputs and outputs were adjusted to  231  accommodate an urban system. For example, the decomposition rate of aboveground CWD in the urban forest was set to zero. This was used as a means to remove any influence of this component as it was assumed that CWD is removed from urban forests. This is in contrast to natural forest where it remains as a component. Before finalizing the input data, sensitivity analyses should be carried out. These are useful in setting the boundary conditions of the site. They indicate the influence of the various inputs on the results and the accuracy and precision required of the inputs. They are also useful in interpreting the results. The sensitivity analyses indicated that the most sensitive parameters were NPP input, amount of annual NPP allocated to a maximum monthly NPP, soil C content, drainage, texture, and soil C:N ratios. The model was not sensitive to percent tree respiration, LAI, or soil bulk density. The results of the simulations were compared to the findings of the field investigation. Based on this, CENTURY is a suitable tool to assess the impact of forest, agriculture (hay), and urban (trees and lawn) on C storage and assimilation rates. The output was generally within the range occurring in the study area. The model did produce higher amounts of C in the fine root component compared to the findings of thefieldinvestigation. Fine roots are subject to dieback on an on-going basis, and the model appears to simulate this well. The difference between the model and the field investigation is attributed to the fact that the model simulates root biomass throughout the year, accommodating high and low growth periods, on a monthly basis. In thefieldinvestigation, roots were sampled only in the winter, the period of lowest root biomass. The results of the computer simulations indicate that the natural forest sequestered the largest amount of C over time, 5% more than the urban forest, approximately three times as  232  much as the manured hay crop, and five times as much as lawn. In terms of the aboveground components, the natural forest sequestered 40 times more C than hay, and 625 times more than lawn indicating the role of (woody) non-harvested vegetation over harvested vegetation. The largest soil C pool occurred under the manured hay system of which the average was 34% greater than that which occurred under the natural forest and 55% more that that which occurred under lawn. The differences in the total amount of C sequestered in the forest and manured hay system is narrowed as a result of the high C content of the hay soils. In the forest system, soils represent 20% of the total C budget, and in hay and lawn systems, the soils represented 89% and 96%, respectively, confirming the importance of soils as a major C pool. These are the same ranges that occurred in the field investigation indica