Bentham, M.

Ha, T.

Pennock, D.

English

eCommons@USASK

Quantifying changes in biomass and soil organic carbon after abandonment of croplands in Saskatchewan

University of Saskatchewan Research Archive

Quantifying changes in biomass and soil organic carbon after abandonment of croplands in Saskatchewan  Thuan Ha PhD candidate Soil Science Department March 5, 2013  Introduction • Global temperature has increased in the last century and is projected to continue to rise in 21st century. • The level of carbon dioxide in atmosphere is closely related to global temperature 2 http://www.skepticalscience.com/ Introduction Pg./year Forest Agriculture Agriculture. Abandonment Introduction BIOMASS BIOMASS Secondary Forest In Saskatchewan:  about 191 Km2 croplands were abandoned in period from 1986 to 2006 (Statistics Canada, 2006). Carbon source TIME TIME Carbon sink Agriculture and agri-food Canada requires scientific based estimate for international reporting. 5 Research questions 1. What percent of agriculture lands were abandoned in the last 50 years (1960 - present)?  2. How much has carbon (Mg ha-1 ) accumulated and distributed in soil and forest after agriculture abandonment; what is the rate?  3. What is the pattern of carbon stocks and likely changes in abandoned croplands in coming decades?  6 Main objectives 1. Delineate abandoned croplands that have regenerated (10 to 60 years) and converted into shrub lands or forests.   2. Quantify carbon stocks and calculate carbon accumulation rates of abandoned croplands  3. Simulate and predict future carbon stocks and changes in forests and soils in abandoned croplands.  Abandoned croplands Abandoned croplands refer to land that used to be cultivated but cultivation activities have ceased for at least 10 years and regeneration to shrub or forest has begun.  Year 0            year 10                            year 30                           year 70                       year 150 Study areas Canada Bright sand  lake Witchekan  Lake TURTLEFORD - SASKATCHEWAN  53°24'00.000" N 108°57'00.000" W Red berry 8 1960    1970        1980                1990      2000                    2010 SPOT  Archived Aerial Photos Field data 9 Data collection Witchekan Lake, SK Remotely sensed data & GIS data: GIS Data collection Croplands Matured forests Shugart et al, 2001 Field data:       Non- Tree biomass + Tree Biomass  +  Soil organic carbon  +   Leaf area index  A series of abandoned cropland sites established at different time (chronosequences approach). Croplands and matured forests as reference data 10 Abandoned Croplands 11 Change detection approach • Approach 1: Visual on-screen change detection and digitalization (VOS)    Co-georeference in a coordinate system   Manual change detection   Digitalization   Validation         Objective 1:  Delineate abandoned croplands 12 Change detection methods 1970 vs. 2012 Objective 1:  Delineate abandoned croplands 12 13 Results from pilot study (Turtleford, SK) 14x14 km Objective 1:  Delineate abandoned croplands 13 14 Results from pilot study (Turtleford, SK) Site name Area (ha) Approx. Age  BS N10 17.61 10 - 15 BS E45 15.31 43 - 47  BS L60 39.07 60  BS K70 14.84 70  BS S40 22.27 37 - 40  Red A17 7.08 17  Red B17 32.37 17  WL A47 11.49 47 Sum 160.04 Objective 1:  Delineate abandoned croplands Visual on-screen change detection: • Simple technique • Provides high accuracy • Time consuming • Labour intensive.  15 Post-classification method Objective 1:  Delineate abandoned croplands • Approach 2: Post-classification comparison change detection using aerial photographs and SPOT images  Aerial photos (t-1; t-2; t-3) Land cover map time t0; t-1; t-2; t-3 Object-Oriented classification Post-classification change detection Land cover  change map (Cropland/shrub/forest) Accuracy assessment  Classification scheme Training samples SPOT (t0) Aerial photos Stereoscopic Field data Example of object-oriented classification Segmentation scale: 205; Segmentation colour: 0.5 16 • Maps of abandoned croplands • Stand age map • Conversion rate  Expected results Puyravaud (2003) Where: r is the change rate; t is time; and A is area 𝑟 = 1𝑡2 − 𝑡1∗ ln 𝐴2𝐴1 Objective 1:  Delineate abandoned croplands 17 4. Litter 18 Data collection • Define Carbon pools Objective 2  Quantify carbon stocks and calculate carbon accumulation rates of abandoned croplands Image source: http://carbon.sref.info/components 3. Dead biomass   5. Soil organic matter 2. Live below ground biomass Forest floor Soil Detritus 1 Detritus 2 1. Live above ground biomass IPCC, 2006 Roots 19 Data collection and sample design • Sample design         Sample points were designed in a systematic grid of 3x3 Pennock, 2006 • Data collection         20 Data collection • Soil sampling   45 cm depth 15 cm increment  Fitzsimmons et al., 2004  Root-cutting soil coring  Soil sample rings  Truck-mounted soil sampler 21 Biomass pools Indicators Methods for data collection Plot type Living trees  > 3 m in height Non-destructive measurement of diameter at breast high or DBH (1.3m), tree species and tree height (H);  Non-destructive leaf area index (LAI) Tree Dead standing (detritus 1) > 10 cm in diameter Non-destructive, apply cylinder equation Tree Shrub vegetation 0.5-3 m in height Average height Harvest and separate via tree species, weight wet and subsample (~10%) for oven dry. Shrub Lying dead wood, dead branches (detritus 2) 2-10 cm in diameter Destructive, oven dry, weight Shrub Grass <0.5m in height Destructive, weight wet and subsample for oven dry weight. Herb Litter 0-2 cm in diameter Destructive, oven dry, weight Herb Soil  15 cm increment to 45 cm depth Soil coring, soil pit  soil Data collection methods Parameters Methods for biomass estimation Living trees • Trembling aspen and balsam poplar, white spruce : Lambert  et al. (2005). • Choke cherry, and willow: Ter-Mikaelian et al. (1997). • Others: Bond-Lamberty et al. (2002); Bella and Defranceschi (1980); Stanek and State  (1978). Detritus 1 V= πr2h (cylinder equation for dead standing) Shrub vegetation Wet mass  dry mass   carbon  Grass/mosses and lichens Wet mass   dry mass    carbon Detritus 2 Wet mass   dry mass  carbon   Litter Wet mass    dry mass    carbon  22 Above-ground biomass estimation methods         Tree biomass estimation methods Below-ground biomass and soil carbon    23 Parameter Unit Method Coarse roots  Mg C ha-1 Allometric equations  (Li et al. 2003) Bulk density gcm-3 Oven dry (M/V) Soil organic carbon Mg C ha-1 Dry combustion (Leco CR-12 Carbon Analyser) SOC density using Meersmans’s equation ( Meersmans et al. 2008) Root biomass and Soil C estimation methods Objective 2  Quantify carbon stocks and calculate carbon accumulation rates of abandoned croplands Tree biomass and soil C estimation Total C sequestration = rate * area Change rate  C= C t2 – C t1  𝑡2−𝑡1 C stock = ∑(𝐶𝐿𝐿𝐿𝐿𝐿𝐿 𝑏𝐿𝑏𝑏𝑏𝑏𝑏 + 𝐶𝐷𝐷𝑏𝐷 𝑏𝐿𝑏𝑏𝑏𝑏𝑏 + 𝐶𝐿𝐿𝑡𝑡𝐷𝐿  + 𝐶𝑆𝑏𝐿𝑆) 00.511.522.530 20 40 60 80 100 120 140Time since abandonment (yrs)   Forest Foote and Grogan, 2007  Carbon content (kg/m2 ) 24 - 8 abandoned sites - 4 croplands - 4 mature forests Number sites and samples collected in  SK 2012:  Initial work - 231 soil samples - 12 soil pits - 1656 tree measurements - 66 tree boring samples - 360 LAI sample points - 68 litter samples - 65 herb samples - 50 dead detritus samples - A number of shrub samples in different species. Total of 16 sites Results General info:  • Range age: 10 -70 years • Range dbh: 1-30 cm • Soil order: Chernozem • Range of LAI: 0.1-5.0 • Dominant species: Trembling aspen, Birch willow, Balsam poplar, ,,,,  26 Contribution of the non-tree above ground biomass pool Biomass proportion of the non-tree above ground biomass pools 0%10%20%30%40%50%60%70%80%90%100%10 17 18 40 45 47 60 70Dry biomass distribution (%) Site age (year) HERBSHRUBDEADLITTERSpatial distribution of C Objective 3  Estimate spatial distributions of forest and soil carbon at landscape scale.  29 SPOT data NDVI/EVI/RSR Potithep et al. (2010) Field data LAI, AGB, Age Regression model f-LAI - NDVI LAI  map LAI - AGE AGE  map LAI – f-AGB Initial AGB  map Land cover map C map multiple regression Regression kriging Geographically weighted regression Predict future C stocks and changes Objective 4  Simulate and predict carbon stocks and changes in forests and soils in abandoned croplands. • Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is an aspatial, stand- and landscape-level modeling framework that simulates the dynamics of all forest carbon stocks required under the Kyoto Protocol.  • This is a well established model but this model was built from data that measured from mature forest; So, it is not applicable directly to my research.  • Here, new dataset from shrub and younger forest will be used to enable the model for research objective 4    Kurz et al. 2009 30 CBM-CFS3 model CBM-CFS3 model • Output variables • Expectation results Kull et al. 2007 31  CENTURY model Soil Organic Matter Model Environment CENTURY was originally developed to simulate soil C, N, P, S dynamics in the prairie grasslands of North America (Parton et al., 1987).  http://nrel.colostate.edu Submodels:  Soil Organic Matter Nitrogen  Phosphorus  Sulfur  Crop and Grassland  Forest  Savanna  Other Simulated Effects  Depth Distribution of Organic C Weather Data Parameterization Simulate carbon stocks and changes in soil CENTURY MODEL Kirschbaum and Paul, 2002 C input from Vegetation Initial soil C Decomposition  Rate  C  losses CENTURY MODEL - Expected results  • Carbon stock (Mg C ha-1 yr-1) in different stand ages (year).  • The absolute rate of carbon change (Mg C ha-1 yr-1 ) over a 60 year period under cropland to forest conversion. Objective 4  Simulate and predict future carbon stocks and changes in forests and soils in abandoned croplands. Berg and Laskowski, 2005 Model evaluation Objective 4  Simulate and predict future carbon stocks and changes in forests and soils in abandoned croplands. 1. Relative root mean square of error (RMSE) 2. Model efficiency 3. Relative error 4. Coefficient of determination 5. Mean different Chiti et al. 2010 SUMMARY 1. Main objectives: 1. Delineating abandoned croplands (since 1960) and identifying the change rate. 2. Quantifying carbon stocks and changes. Estimate C spatial distribution. 3. Simulating temporal distribution of carbon in different pools.  2. Input data: 1. Field data (using chronosequence approach) 2. Remotely sensed data (Aerial photos, Landsat, SPOT).  3. Method: 1. Visual on-screen change detection and digitalization and post-classification comparison change detection. 2. Multiple regression model for simulating aboveground biomass. 3. CMB-CFS3 and CENTURY model for simulating carbon stocks and changes in abandoned croplands.   Acknowledgement  • Dr. Dan Pennock • Dr. Murray Bentham Soil Science Dept. U of S  • Advisory committee members • Dr. Xulin Guo • Dr. Colin Laroque Geography and Planning – U of S Geography and Environment Dept. Mount Alison University, NB • Dr. Leah Brannen  • Darrel Cerkowniak • Paul Krug Land Resource Unit – U of S  • Kendra Purton • Jeremy Kiss Graduate Students  • Dr. Pennock • Dr. Ted Huffman  • Vietnam international education development (VIED)  • Soil Science Department • College of Graduate and Research  Soil Science Dept. U of S Land Use and Management,  AAFC  Ministry of Education of VN    U of S U of S Thank you! 38 Reference • Chiti, T., D. Papale, P. Smith, D. Dalmonech, G. Matteucci, J. Yeluripati, M. Rodeghiero, and R. Valentini. 2010. Predicting changes in soil organic carbon in mediterranean and alpine forests during the Kyoto Protocol commitment periods using the CENTURY model.  Soil Use and Management 26:475-484. • Fitzsimmons, M.J., D.J. Pennock, and J. Thorpe. 2004. Effects of deforestation on ecosystem carbon densities in central Saskatchewan, Canada.  Forest Ecology and Management 188:349-361. • Foote, R.L., and P. Grogan. 2010. Soil Carbon Accumulation During Temperate Forest Succession on Abandoned Low Productivity Agricultural Lands.  Ecosystems 13:795-812. • IPCC. 2006. Good Practice Guidance for Land Use, Land-Use Change and Forestry Global Environmental Strategies for the IPCC, Hayama, Kanagawa, Japan. • Potithepa, S., N.K. Nasaharab, H. Muraokac, S. Nagaia, and R. Suzukia. 2010. What is the actual relationship between LAI and VI in a deciduous broadleaf forest?  In Koji Kajiwara, et al. (eds.) Proc. Technical Commission VIII Symposium Networking the World with Remote Sensing Japan2010. ISPRS. • Puyravaud, J.P. 2003. Standardizing the calculation of the annual rate of deforestation. (in English) Forest Ecology and Management 177:593-596. • Statistics Canada. 2008. Total farm area, land tenure and land in crops, by province (Census of Agriculture, 1986 to 2006). Available at http://www40.statcan.gc.ca/l01/cst01/agrc25a-eng.htm (accessed Janury 20). Statistics Canada, Canada.  

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Quantifying changes in biomass and soil organic carbon after abandonment of croplands in Saskatchewan

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