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Socioecological Carbon Production in Managed Agricultural-Forest Landscapes (iSEC)

Exploring the new concept of social C flux and its effect on the carbon cycle in managed agriculture and forest landscapes.

Project Overview

Land use, land cover changes, and ecosystem-specific management practices are increasingly recognized for their roles in mediating the climatic effects on ecosystem structure and function. As demonstrated by some scholars, human activities can influence C fluxes and storage far more than climatic changes (IPCC 2014).

Our understanding and forecasting of ecosystem C fluxes cannot rely solely on conventional biophysical regulations at any scale, from the local ecosystem to the globe. We must quantify the magnitude of the C fluxes from managed ecosystems and landscapes over the lifetime of the C cycle and deduct the various energy inputs during management from the amount of C sequestered by an ecosystem (West & Marland 2003). For example, conventional crop management often includes tillage, fertilization, irrigation, applications of pesticides and herbicides, harvesting, transportation to the market, land conversion, etc. All of these activities require a CO2-equivalent (CO2eq) amount of energy (“social C flux”) to offset the actual amount of C sequestered by the ecosystems and landscapes. A complete life cycle assessment (LCA) is needed to account for the actual sequestration strength at different spatial and temporal scales.

Our overall objective is to quantify the landscape-scale C fluxes at annual scale of both managed agricultural-forest landscapes and people, using the Kalamazoo watershed in southwestern Michigan as our testbed.

Questions to be answered

  1. (1) What are the quantitative contributions of land cover change, specific management practices, and climate changes (means and extremes) to the social and physical C fluxes of managed ecosystems and landscapes?
  2. (2) What are the spatial and temporal changes of their contributions in managed agricultural-forest landscapes?
  3. (3) How will future land use changes (including alternative management practices) impact C sequestration in an upper, mid-latitude managed ecosystem?

Acknowledgements

Funded by NASA Carbon Cycle & Ecosystems (CC&E)
Download project summary

Recent Activity

  • March 20, 2017: Annual Legislative Breakfast hosted by the Kalamazoo Environmental Council.
  • May-July 2017: Five flux towers can be found across the Kalamazoo Watershed representing urban, marsh, forest, and agricultural land.
  • July 10, 2017:
  • Changes in land cover type across the Kalamazoo Watershed (Landsat 30m resolution data) demonstrate the increasing rates of urbanization and from 1996 to 2011. Credit: Rong Zhang

Conceptual Framework and Hypothesis

Our overarching hypothesis is that social C flux is more responsible than physical C flux for the dynamics, and especially the uncertainty, of the cumulative CO2eq production of these intensively-managed landscapes. However, their proportions vary significantly among the landscapes and over history because of the great variations in land conversions, land use practices, climatic changes and extremes in the watershed.

flow chart titled: Future Climate: RCP Scenarios, CMIP5 or 6

Figure 1. (above) Proposed research components and their linkages for process-based predictions of the spatiotemporal changes in CO2eq production that will be quantified by estimating “social C flux”, and “physical C flux” at contrasting landscapes (i.e., different land cover compositions) within the Kalamazoo Watershed as well as the entire watershed (Fig. 2, below).

Life cycle assessment (LCA) will be employed for major patch types to quantify the C production at different temporal scales. The statistical downscaling modeling will be used to predict future local climate from Representative Concentration Pathways scenarios. Bayesian structural equation models (SEM) will be constructed to explore the contributions of climate change and human activities.

an image depicting the land cover types of the Kalamazoo, MI watershed

Figure 2. (above) Current land cover of the Kalamazoo Watershed (NLCD), which includes 127 sub-watersheds (USGS). The entire watershed will be examined for the changes of CO2eq during a 40-year period (1978–2018) using Landsat/Sentinel with the climate and human activities following our working framework (Fig. 1), while four contrasting landscapes will be quantified with high-resolution RS data and historical records and survey statistics over an 80-year period (1938–2018).

Research Tasks

Task 1: Dynamics of Physical C Fluxes

Quantify the changes of the physical C flux on an annual scale, which will be converted to CO2eq, by integrating: (1) remotely-sensed land cover type and other surface properties; (2) geospatial records of climate, vegetation, soil, and management practices for model parameterization; (3) direct measurements of net ecosystem exchange of CO2 using EC flux towers for model validation; and (4) a customized ecosystem model (i.e. CLM).

Task 2: Dynamics of Social C Fluxes

Estimate the social C fluxes of major management practices for different land cover types by classifying historical land cover, identifying land ownership, and by surveying historical management practices of individual land-owners (parcel scale). Back-of-the-envelope calculations will be applied to scale up the CO2eq fluxes to the landscapes and the watershed.

Task 3: The dynamics and the regulations of CO2eq in time and space

Diagnose the mechanistic/empirical causal relationships based on biophysical models and SEM, and to quantify the ecosystem, landscape, and watershed C fluxes at multiple temporal scales and under alternative management/climate scenarios.

Open Data Resources

In alliance with NASA, our researchers openly share their data with the broader community. The latest information and resources from our project can be found here for exploration and education.

Data use policy

Use of this data is for non-profit, personal, or educational use only. To publish this data or apply it in other for-profit endeavours, please contact Dr. Jiquan Chen for information. For help interpreting the data, contact the author listed. Information provided here does not conflict with our committment to confidentiality or expose personal information from survey participants.

Flux Tower Data

Tower Name Location Landcover Type Year
KEF Kellogg Experimental Forest Forest 2017
URB Battle Creek Math and Science Center Urban 2017
WET Allegan County, private residence Wetland 2017
AGR_UR Albion County, private farmland Agriculture, unirrigated 2017
AGR_IR Albion County, private farmland Agriculture, irrigated 2017

Remote Sensing Data

Data Type Description Year
Shape Files Landcover change shape files. Preview results: 2001 | 2011 2001-2011
Landsat Landsat images by year 2017
Venus Venus images by year 2017
MODIS MODIS images by year 2017
Historical Aerial Image Aerial images by year 2017

Social Data

Name Source Link Download/Year
Michigan Carbon Dioxide Emissions from Fossil Fuel Consumption US EIA 1980-2015
Energy-Related Carbon Dioxide Emissions at the State Level US EIA 2000-2014
Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program 2014
GHG calculator by EPA EPA Download

CLM Data

Data Type Description Year

Other Data

Data Type Description Files
Stream gauge data Gauge height, discharge, pH, conductivity turbidity, dissolved oxygen (download 75,547KB)

 

References

  1. Abraha, M., Gelfand, I., Hamilton, S. K., Shao, C., Su, Y.-J., Robertson, G. P., & Chen, J. (2016). Ecosystem Water-Use Efficiency of Annual Corn and Perennial Grasslands: Contributions from Land-Use History and Species Composition. Ecosystems, 1-12.
  2. Amiro, B.D., A.G. Barr, J.G. Barr, T.A. Black, R. Bracho, M. Brown, J. Chen, et al. (2010). Ecosystem carbon dioxide fluxes after disturbance in forests of North America. Journal of Geophysical Research - Biogeosciences 115(G4), G00K02. DOI: 10.1029/2010JG001390
  3. Arhonditsis, G. B., Stow, C. A., Steinberg, L. J., Kenney, M. A., Lathrop, R. C., McBride, S. J., & Reckhow, K. H. (2006).Exploring ecological patterns with structural equation modeling and Bayesian analysis. Ecological Modelling, 192(3–4), 385-409.
  4. Bass, D.G. (2009). Inferring dissolved phosphorus cycling in a TMDL watershed using biogeochemistry and mixed linear models. Ph.D. Thesis, Michigan State University, East Lansing, Michigan.
  5. Champman, K. A., and Brewer, R. (2008). Prairie and savanna in southern Lower Michigan: history, classification, ecology. Mich. Bot., 47:1-48.
  6. Chen, J., Brosofske, D. K., Noormets, A., Crow, R. T., Bresee, K. M., Le Moine, et al. (2004). A Working Framework for Quantifying Carbon Sequestration in Disturbed Land Mosaics. Environmental Management, 33(1), S210-S221.
  7. Chen, J., Davis, K. J., & Meyers, T. P. (2008). Ecosystem–atmosphere carbon and water cycling in the upper Great Lakes Region. Agricultural and Forest Meteorology, 148(2), 155-157.
  8. Chen, J., John, R., Shao, C., Fan, Y., Zhang, Y., Amarjargal, Dong, G. (2015). Policy shifts influence the functional changes of the CNH systems on the Mongolian plateau. Environmental Research Letters, 10(8), 085003
  9. Chu, H., Chen, J., Gottgens, J. F., Desai, A. R., Ouyang, Z., & Qian, S. S. (2016). Response and biophysical regulation of carbon dioxide fluxes to climate variability and anomaly in contrasting ecosystems in northwestern Ohio, USA. Agricultural and Forest Meteorology, 220, 50-68.
  10. Cressie, N. & Wikle., C. (2011). Statistics for spatio-temporal data. Wiley press.
  11. Drewniak, B., Song, J., Prell, J., Kotamarthi, V. R., & Jacob, R. (2013). Modeling agriculture in the Community Land Model. Geosci. Model Dev., 6(2), 495-515.
  12. Estoque, R. C., & Murayama, Y. (2015). Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205-217.
  13. Euskirchen, E. S., Chen, J., Li, H., Gustafson, E. J., & Crow, T. R. (2002). Modeling landscape net ecosystem productivity (LandNEP) under alternative management regimes. Ecological Modelling, 154(1–2), 75-91.
  14. Fan, Y., Wu, R., Chen, J., & Apul, D. (2015). A Review of Social Life Cycle Assessment Methodologies. In S. S. Muthu (Ed.), Social Life Cycle Assessment: An Insight (pp. 1-23). Singapore: Springer Singapore.
  15. Fongers, D. (2008). Kalamazoo River Watershed Hydrologic Study. Retrieved from http://kalamazooriver.org/wp-content/uploads/2012/12/DEQ-Kalamazoo-River-Hydrologic-Study-2008.pdf
  16. Gelfand, A. E., & Smith, A. F. M. (1990). Sampling-Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85(410), 398-409.
  17. Gelfand, I., Zenone, T., Jasrotia, P., Chen, J., Hamilton, S. K., & Robertson, G. P. (2011). Carbon debt of Conservation Reserve Program (CRP) grasslands converted to bioenergy production. Proceedings of the National Academy of Sciences, 108(33), 13864-13869.
  18. Grace, J. B., Anderson, T. M., Olff, H., & Scheiner, S. M. (2010). On the specification of structural equation models for ecological systems. Ecological Monographs, 80(1), 67-87.
  19. Hamilton, S. K., Hussain, M. Z., Lowrie, C., Basso, B., & Robertson, G. P. (in review) Evapotranspiration response to land cover and climate change in a Midwest U.S. watershed. Scientific Reports.
  20. Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, et al. (2013). The Community Earth System Model: A Framework for Collaborative Research. Bulletin of the American Meteorological Society, 94(9), 1339-1360.
  21. Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G. et al. (2011). Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change, 109(1), 117-161.
  22. IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (Eds.)]. IPCC, Geneva, Switzerland, 151 pp.
  23. Koellner, T. (2002). Land use in product life cycles and its consequences for ecosystem quality. The International Journal of Life Cycle Assessment, 7(2), 130-130.
  24. Koellner, T., de Baan, L., Beck, T., Brandão, M., Civit, B., Margni, M., et al. (2013). UNEP-SETAC guideline on global land use impact assessment on biodiversity and ecosystem services in LCA. The International Journal of Life Cycle Assessment, 18(6), 1188-1202.
  25. Koellner, T., & Geyer, R. (2013). Global land use impact assessment on biodiversity and ecosystem services in LCA. The International Journal of Life Cycle Assessment, 18(6), 1185-1187.
  26. Koellner, T., & Scholz, R. W. (2006). Assessment of land use impacts on the natural environment. The International Journal of Life Cycle Assessment, 13(1), 32-48. doi:10.1065/lca2006.12.292.2
  27. Lal, R. (2011). Reducing emissions and sequestering carbon in agroecosystems. Food Policy, 36, S33-S39.
  28. Levasseur, A., P. Lesage, M. Margni, L. Deschênes, & R. Samson. (2009). How dynamic LCA can bring consistency in assessing global warming mitigation scenarios: Paper presented at Life Cycle Assessment IX: toward the global life cycle economy. Boston, MA
  29. Levis, S., Badger, A., Drewniak, B., Nevison, C., & Ren, X. (2016). CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5. Climatic Change, 1-15.
  30. Levis, S., Hartman, M. D., & Bonan, G. B. (2014). The Community Land Model underestimates land-use CO2 emissions by neglecting soil disturbance from cultivation. Geosci. Model Dev., 7(2), 613-620.
  31. Mao, J., Ricciuto, D. M., Thornton, P. E., Warren, J. M., King, A. W., Shi, X., et al. (2016). Evaluating the Community Land Model in a pine stand with shading manipulations and 13CO2 labeling. Biogeosciences, 13(3), 641-657.
  32. Marland, G., West, T. O., Schlamadinger, B., & Canella, L. (2003). Managing soil organic carbon in agriculture: the net effect on greenhouse gas emissions. Tellus B, 55(2), 613-621.
  33. Millar, N., Robertson, G.P., Grace, P.R., Gehl, R.J. and Hoben, J.P. (2010). Nitrogen fertilizer management for nitrous oxide (N2O) mitigation in intensive corn (Maize) production: an emissions reduction protocol for US Midwest agriculture. Mitig Adapt Strateg Glob Change, 15,185–204.
  34. Milà i Canals, L., Bauer, C., Depestele, J., Dubreuil, A., Freiermuth Knuchel, R., Gaillard, et al. (2007). Key elements in a framework for land use impact assessment within LCA (11 pp). The International Journal of Life Cycle Assessment, 12(1), 5-15.
  35. Mladenoff, D. J. (2004). LANDIS and forest landscape models. Ecological Modelling, 180(1), 7-19.
  36. Noriega, A. E., & de Alba, E. (2001). Stationarity and structural breaks — evidence from classical and Bayesian approaches. Economic Modelling, 18(4), 503-524.
  37. Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, et al. (2013). Technical Description of version 4.5 of the Community Land Model (CLM). Boulder, CO: National Center for Atmospheric Research. Retrieved from http://n2t.net/ark:/85065/d74f1q4q
  38. Papale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, et al. (2015). Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks. Journal of Geophysical Research: Biogeosciences, 120(10), 1941-1957.
  39. Poursanidis, D., Chrysoulakis, N., & Mitraka, Z. (2015). Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping. International Journal of Applied Earth Observation and Geoinformation, 35, Part B, 259-269.
  40. Reap, J., Roman, F., Duncan, S., & Bras, B. (2008). A survey of unresolved problems in life cycle assessment. The International Journal of Life Cycle Assessment, 13(5), 374-388.
  41. Robertson, G. P., Paul, E. A., & Harwood, R. R. (2000). Greenhouse Gases in Intensive Agriculture: Contributions of Individual Gases to the Radiative Forcing of the Atmosphere. Science, 289(5486), 1922-1925.
  42. Saad, R., Margni, M., Koellner, T., Wittstock, B., & Deschênes, L. (2011). Assessment of land use impacts on soil ecological functions: development of spatially differentiated characterization factors within a Canadian context. The International Journal of Life Cycle Assessment, 16(3), 198-211.
  43. Schaefer, K., Schwalm, C. R., Williams, C., Arain, M. A., Barr, A., Chen, et al. (2012). A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis. Journal of Geophysical Research: Biogeosciences, 117(G3), n/a-n/a. doi:10.1029/2012JG001960
  44. Schaetzl, R. J., Darden, J. T., & Brandt, D. S. (2009). Michigan geography and geology. New York: Custom Publishing.
  45. Shao, C., Chen, J., & Li, L. (2013). Grazing alters the biophysical regulation of carbon fluxes in a desert steppe. Environmental Research Letters, 8(2), 025012.
  46. Smith, P., Andrén, O., Karlsson, T., Perälä, P., Regina, K., Rounsevell, M., & Van Wesemael, B. (2005). Carbon sequestration potential in European croplands has been overestimated. Global Change Biology, 11(12), 2153-2163.
  47. The United Nations, Framework Convention on Climate Change. (2015). Adoption of the Paris Agreement. Retrieved from: https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf
  48. West, T. O., & Marland, G. (2003). Net carbon flux from agriculture: Carbon emissions, carbon sequestration, crop yield, and land-use change. Biogeochemistry, 63(1), 73-83.
  49. West, T. O., & Post, W. M. (2002). Soil Organic Carbon Sequestration by Tillage and Crop Rotation: A Global Data Analysis. Soil Science Society of America Journal, 66, 1930-1946.
  50. Wiedmann, T. and Minx, J. (2008). A definition of ‘carbon footprint’. Ecological Economics Research Trends, 1, 1-11.
  51. Wu, R., Yang, D., & Chen, J. (2014). Social Life Cycle Assessment Revisited. Sustainability, 6(7), 4200.
  52. Xiao, J., Chen, J., Davis, K. J., & Reichstein, M. (2012). Advances in upscaling of eddy covariance measurements of carbon and water fluxes. Journal of Geophysical Research: Biogeosciences, 117(G1), n/a-n/a. doi:10.1029/2011JG001889
  53. Xie, J., Chen, J., Sun, G., Zha, T., Yang, B., Chu, H., et al. (2016). Ten-year variability in ecosystem water use efficiency in an oak-dominated temperate forest under a warming climate. Agricultural and Forest Meteorology, 218–219, 209-217.
  54. Zenone, T., Chen, J., Deal, M. W., Wilske, B., Jasrotia, P., Xu, et al. (2011). CO2 fluxes of transitional bioenergy crops: effect of land conversion during the first year of cultivation. GCB Bioenergy, 3(5), 401-412.
  55. Zenone, T., Gelfand, I., Chen, J., Hamilton, S. K., & Robertson, G. P. (2013). From set-aside grassland to annual and perennial cellulosic biofuel crops: Effects of land use change on carbon balance. Agricultural and Forest Meteorology, 182–183, 1-12.
  56. Zimmerman, P.R., Price, M, Peng, C., Capehart, W.J., Updegraff, K., Kozak, P., et al. (2005). C-lock (patent pending): a system for estimating and certifying carbon emission reduction credits for the sequestration of soil carbon on agricultural land. Mitigation and Adaptation Strategies for Global Change, 10, 307–331.

Products and Presentations

Publications

  1. Lei, C., M. Abraha, Y.J. Su, and J. Chen. Variability of root production in bioenergy systems using ingrowth cores and eddy covariance. Journal of Plant Ecology (to be submitted in Dec. 2017)

Presentations

  1. Chen, J., K. Dahlin, R. John, G. Shirkey, S. R. Wu, P. Robertson, S. Hamilton, L. Cooper, D. Lusch, and A. Karnieli, R. Lafortezza, and G. S. Labini. Socioecological Carbon Production in Managed Agricultural-Forest Landscapes. 2017 Joint NACP & AmeriFlux PI Meeting, March 27-30, 2017, North Bethesda, MD, USA
  2. Chen, J., K. Dahlin, R. John, G. Shirkey, S. R. Wu, P. Robertson, S. Hamilton, L. Cooper, D. Lusch, and A. Karnieli, R. Lafortezza, and G. S. Labini. Socioecological Carbon Production in Managed Agricultural-Forest Landscapes. Worldcover 2017 Conference, 14–16 March 2017, Rome, Italy

Team Members

Student opportunities
Download graduate position details
Two PhD opportunities are available. Those interested in applying should contact Dr. Jiquan Chen with your CV, GRE scores, TOEFL (if applicable), and experience for more information.

Dr. Jiquan Chen Professor, PI
Michigan State University
jqchen@msu.edu
Dr. Kyla Dahlin Assistant Professor, Co-PI
Michigan State University
kdahlin@msu.edu
Dr. Ranjeet John Research Associate, Co-PI
Michigan State University
ranjeetj@msu.edu
Dr. Phil Robertson Collaborator
Kellogg Biological Station (GLBRC)
robert30@msu.edu
Dr. Steve Hamilton Collaborator
Kellogg Biological Station (LTER)
hamilton@msu.edu
Dr. David Lusch Collaborator, RS/GIS
Michigan State University
lusch@msu.edu
Lauren Cooper Collaborator, Forest Climate Program
Michigan State University
ltcooper@msu.edu
Dr. Arnon Karnieli Collaborator
Ben Gurion University, Israel
karnieli@bgu.ac.il
Giovanni Sylos Labini Collaborator Planetek, Italy website
  Gabriela Shirkey

Graduate Student, Michigan State University