Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T05:16:29.207Z Has data issue: false hasContentIssue false

4 - Five Decades of Modeling Supporting the Systems Ecology Paradigm

Published online by Cambridge University Press:  25 February 2021

By
William J. Parton ,
Stephen J. Del Grosso ,
Eleanor E. Campbell ,
Melanie D. Hartman ,
N. T. Hobbs ,
John C. Moore ,
David M. Swift ,
David S. Schimel ,
Dennis S. Ojima  and
Michael B. Coughenour
...Show all authors
Edited by
Robert G. Woodmansee ,
John C. Moore ,
Dennis S. Ojima  and
Laurie Richards
Robert G. Woodmansee
Affiliation:
Colorado State University
John C. Moore
Affiliation:
Colorado State University
Dennis S. Ojima
Affiliation:
Colorado State University
Laurie Richards
Affiliation:
Colorado State University
Get access

Summary

Ecosystem modeling, a pillar of the systems ecology paradigm (SEP), addresses questions such as, how much carbon and nitrogen are cycled within ecological sites, landscapes, or indeed the earth system? Or how are human activities modifying these flows? Modeling, when coupled with field and laboratory studies, represents the essence of the SEP in that they embody accumulated knowledge and generate hypotheses to test understanding of ecosystem processes and behavior. Initially, ecosystem models were primarily used to improve our understanding about how biophysical aspects of ecosystems operate. However, current ecosystem models are widely used to make accurate predictions about how large-scale phenomena such as climate change and management practices impact ecosystem dynamics and assess potential effects of these changes on economic activity and policy making. In sum, ecosystem models embedded in the SEP remain our best mechanism to integrate diverse types of knowledge regarding how the earth system functions and to make quantitative predictions that can be confronted with observations of reality. Modeling efforts discussed are the Century ecosystem model, DayCent ecosystem model, Grassland Ecosystem Model ELM, food web models, Savanna model, agent-based and coupled systems modeling, and Bayesian modeling.

Keywords

ecosystem modelssimulation modeling of ecosystemssystems approachCentury ecosystem modelDayCent ecosystem modelGrassland Ecosystem Model ELMfood web modelsSavanna modelagent-based and coupled systems modelingBayesian modeling
Type
Chapter
Information
Natural Resource Management Reimagined
Using the Systems Ecology Paradigm
 , pp. 90 - 130
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdalla, M., Jones, M., Yeluripati, J., et al. (2010). Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture. Atmospheric Environment, 44(25), 2961–70.CrossRefGoogle Scholar
Adair, E. C., Parton, W. J., Del Grosso, S. J., et al. (2008). Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates. Global Change Biology, 14(11), 2636–60.CrossRefGoogle Scholar
Adler, P. R., Spatari, S., D´Ottone, F., et al. (2017). Legacy effects of individual crops affect N2O emissions accounting within crop rotations. Global Change Biology – Bioenergy, 10(2),123–36.Google Scholar
Alcántara, V., Don, A., Well, R., and Nieder, R. (2016). Deep ploughing increases agricultural soil organic matter stocks. Global Change Biology, 22(8), 2939–56.Google Scholar
Asao, S., Parton, W. J., Chen, M., and Gao, W. (2018). Photodegradation accelerates ecosystem N cycling in a simulated California grassland. Ecosphere, 9(8), e02370.Google Scholar
Bachelet, D., Hunt, H. W., and Detling, J. K. (1989). A simulation model of intraseasonal carbon and nitrogen dynamics of blue grama swards as influenced by above-and belowground grazing. Ecological Modelling, 44(3–4), 231–52.CrossRefGoogle Scholar
Billari, F. C., Fent, T., Prskawetz, A., and Scheffran, J. (2006). Agent-based computation modeling: An introduction. In Agent-based Computational Modeling, Contributions to Economics, ed. Billari, F. C., Fent, T., Prskawetz, A., and Scheffran, J.. Heidelberg, Germany: Physica-Verlag, 1–16.Google Scholar
Bista, P., Machado, S., Del Grosso, S. J., Ghimire, R., and Reyes-Fox, M. (2016). Simulating influence of long-term crop residue and nutrient management on soil organic carbon and wheat yield using the DAYCENT model. Agronomy Journal, 108(6), 2554–65.Google Scholar
Boone, R. B., Coughenour, M. B., Galvin, K. A., and Ellis, J. E. (2002). Addressing management questions for Ngorongoro Conservation Area using the Savanna Modeling System. African Journal of Ecology, 40, 138–50.Google Scholar
Boone, R. B., and Galvin, K. A. (2014). Simulation as an approach to social-ecological integration, with an emphasis on agent-based modeling. In Understanding Society and Natural Resources: Forging New Strands of Integration Across the Social Sciences, ed. Manfredo, M., Vaske, J. J., Rechkemmer, A., and Duke, E. A.. Dordrecht, Heidelberg, New York, London: Springer, 179–202.Google Scholar
Boone, R. B., Galvin, K. A., BurnSilver, S. B., et al. (2011). Using coupled simulation models to link pastoral decision making and ecosystem services. Ecology and Society, 16(2), Article 6.CrossRefGoogle Scholar
Boone, R. B., Galvin, K. A., Coughenour, M. B., et al. (2004). Ecosystem modeling adds value to a South African climate forecast. Climatic Change, 64, 317–40.CrossRefGoogle Scholar
Boone, R. B., Lackett, J. M., Galvin, K. A., Ojima, D. S., and Tucker, C. J. (2007). Links and broken chains: Evidence of human-caused changes in land cover in remotely sensed images. Environmental Science & Policy, 10(2), 135–49.Google Scholar
Boone, R. B., and Lesorogol, C. K. (2016). Modelling coupled human–natural systems of pastoralism in East Africa. In Building Resilience of Human–Natural Systems of Pastoralism in the Developing World: Interdisciplinary Perspectives, ed. Dong, S., Kassam, K.-A. S., Tourrand, J. F., and Boone, R. B.. Switzerland: Springer, 251–80.Google Scholar
Bormann, F. H., and Likens, G. E. (1967). Nutrient cycling. Science, 155(3761), 424–9.Google Scholar
Burke, I. C., Yonker, C. M., Parton, W. J., et al. (1989). Texture, climate, and cultivation effects on soil organic matter context in U.S. grassland soils. Soil Science Society of America Journal, 53(3), 800–5.Google Scholar
Campbell, E. E., Parton, W. J., Soong, J. L., et al. (2016). Using litter chemistry controls on microbial processes to partition litter carbon fluxes with the litter decomposition and leaching (LIDEL) model. Soil Biology and Biochemistry, 100, 160–74.CrossRefGoogle Scholar
Campbell, E. E., and Paustian, K. (2015). Current developments in soil organic matter modeling and the expansion of model applications: A review. Environmental Research Letters, 10(12), Article 123004.Google Scholar
Capinera, J. L., Detling, J. K., and Parton, W. J. (1983). Assessment of range caterpillar (Lepidoptera:Saturniidae) effects with a grassland simulation model. Journal of Economic Entomology, 76(5), 1088–94.Google Scholar
Chen, D. X., Hunt, H. W., and Morgan, J. A. (1996). Responses of a C3 and C4 perennial grass to CO2 enrichment and climate change: Comparison between model predictions and experimental data. Ecological Modeling, 87, 11–27.Google Scholar
Coleman, D. C., Cole, C. V., and Elliott, E. T. (1983). Decomposition, organic matter turnover, and nutrient dynamics in agroecosystems. In Nutrient Cycling in Agricultural Ecosystems, ed. Lowrance, R. R., Todd, R. L., Asmussen, L. E., and Leonard, R. A.. Special Publication No. 23. Athens, GA: University of Georgia, College of Agriculture Experiment Stations.Google Scholar
Coughenour, M. B. (1981). Sulfur dioxide deposition and its effect on a grassland sulfur-cycle. Ecological Modeling, 13, 1–16.CrossRefGoogle Scholar
Coughenour, M. B. (1992). Spatial modeling and landscape characterization of an African pastoral ecosystem: A prototype model and its potential use for monitoring drought. In Ecological Indicators, vol. 1, eds. McKenzie, D. H., Hyatt, D. E., and McDonald, V. J.. London and New York: Elsevier Applied Science, 787–810.CrossRefGoogle Scholar
Coughenour, M. B. (1993). SAVANNA – Landscape and Regional Ecosystem Model: Model Description. Fort Collins, CO: Natural Resource Ecology Laboratory, Colorado State University.Google Scholar
Coughenour, M. B. (2000). Ecosystem modeling of the Pryor Mountain Wild Horse Range, executive summary. In United States Geological Survey – USDI: Managers’ Summary – Ecological Studies of the Pryor Mountain Wild Horse Range, 1992–1997, compiled by Singer, F. J. and Schoenecker, K. A.. Fort Collins, CO: US Geological Survey, Midcontinent Ecological Science Center, 125–31.Google Scholar
Coughenour, M. B., and Chen, D. X. (1997). An assessment of grassland ecosystem responses to atmospheric change using linked ecophysiological and soil process models. Ecological Applications, 7, 802–27.Google Scholar
Coughenour, M. B., Ellis, J. E., Swift, D. M., et al. (1985). Energy extraction and use in a nomadic pastoral ecosystem. Science, 230, 619–24.Google Scholar
Coughenour, M. B., and Singer, F. J. (1996). Elk population processes in Yellowstone National Park under the policy of natural regulation. Ecological Applications, 6(2), 573–93.Google Scholar
Cramer, W., Kicklighter, D. W., Bondeau, A., et al. (1999). The intercomparison, and participants of the Potsdam NPP Model. Comparing global models of terrestrial net primary productivity (NPP): Overview and key results. Global Change Biology, 5(S1), 1–15.Google Scholar
Davis, S. C., Parton, W. J., Del Grosso, S. J., et al. (2012). Impact of second-generation biofuel agriculture on greenhouse gas emissions in the corn-growing regions of the US. Frontiers in Ecology and the Environment, 10(2), 69–74.Google Scholar
De Ruiter, P. C., Neutel, A. M., and Moore, J. C. (1995). Energetics, patterns of interaction strengths, and stability in real ecosystems. Science, 269(5228), 1257–60.CrossRefGoogle ScholarPubMed
Del Grosso, S. J., Gollany, H. T., and Reyes-Fox, M. (2016). Simulating soil organic carbon stock changes in agro-ecosystems using CQESTR, DayCent, and IPCC Tier 1 Methods. In Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation, ed. Del Grosso, S. J., Ahuja, L., and Parton, W. J.. Madison, WI: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 89–110.Google Scholar
Del Grosso, S. J., Ogle, S. M., Parton, W. J., and Breidt, F. J. (2010). Estimating uncertainty in N2O emissions from US cropland soils. Global Biogeochemical Cycles, 24, Article GB1009.Google Scholar
Del Grosso, S. J., Parton, W. J., Adler, P. R., et al. (2012). DayCent model simulations for estimating soil carbon dynamics and greenhouse gas fluxes from agricultural production systems. In Managing Agricultural Greenhouse Gases: Coordinated Agricultural Research through GRACEnet to Address Our Changing Climate, ed. Liebig, M., Franzluebbers, A. J., and Follett, R. F.. London: Academic Press, 241–50.Google Scholar
Del Grosso, S. J., Parton, W. J., Mosier, A. R., et al. (2000a). General CH4 oxidation model and comparisons of CH4 oxidation in natural and managed systems. Global Biogeochemical Cycles, 14(4), 999–1019.CrossRefGoogle Scholar
Del Grosso, S. J., Parton, W. J., Mosier, A. R., et al. (2000b). General model for N2O and N2 gas emissions from soils due to denitrification. Global Biogeochemical Cycles, 14(4), 1045–60.Google Scholar
Del Grosso, S. J., White, J. W., Wilson, G., et al. (2013). Introducing the GRACEnet/REAP data contribution, discovery and retrieval system. Journal of Environmental Quality, 42(4), 1274–80.CrossRefGoogle ScholarPubMed
Dietze, M. C., Lebauer, D. S., and Kooper, R. (2013). On improving the communication between models and data. Plant, Cell & Environment, 36(9), 1575–85.Google Scholar
Doherty, J. (2015). Calibration and Uncertainty Analysis for Complex Environmental Models. PEST: Complete Theory and What It Means for Modelling the Real World. Brisbane: Watermark Numerical Computing.Google Scholar
Ellis, J. E., and Swift, D. M. (1988). Stability of African pastoral ecosystems: Alternate paradigms and implications for development. Journal of Range Management, 41, 450–9.Google Scholar
Ellis, J. E., Wiens, J. A., Rodell, C. F., and Anway, J. C. (1976). A conceptual model of diet selection as an ecosystem process. Journal of Theoretical Biology, 60(1), 93–108.Google Scholar
EPA. (2017). Inventory of U.S. greenhouse gas emissions and sinks: 1990–2015. Washington, DC: USEPA. www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.Google Scholar
Fitton, N., Datta, A., Hastings, A., et al. (2014). The challenge of modelling nitrogen management at the field scale: Simulation and sensitivity analysis of N2O fluxes across nine experimental sites using Daily DayCent. Environmental Research Letters, 9(9), Article 095003.Google Scholar
Forrester, J. W. (1968). Principles of Systems. Cambridge, MA: Wright-Allen Press.Google Scholar
Frolking, S. E., Mosier, A. R., Ojima, D. S., et al. (1998). Comparison of N2O emissions from soils at three temperate agricultural sites: Simulations of year-round measurements by four models. Nutrient Cycling in Agroecosystems, 52(2), 77–105.Google Scholar
Fujita, Y., Witte, J.-P. M., and Bodegom, P. M. (2014). Incorporating microbial ecology concepts into global soil mineralization models to improve predictions of carbon and nitrogen fluxes. Global Biogeochemical Cycles, 28(3), 223–38.Google Scholar
Fullman, T. J., Bunting, E. L., Full, G. A., and Southworth, J. (2017). Predicting shifts in large herbivore distributions under climate change and management using a spatially-explicit ecosystem model. Ecological Modeling, 352, 1–18.Google Scholar
Gilmanov, T. G., Parton, W. J., and Ojima, D. S. (1997). Testing the CENTURY ecosystem level model on data sets from eight grassland sites in the former USSR representing wide climatic/soil gradient. Ecological Modelling, 96(1–3), 191–210.Google Scholar
Grant, B. B., Smith, W. N., Campbell, C. A., et al. (2016). Comparison of DayCent and DNDC models: Case studies using data from long-term experiments on the Canadian prairies. In Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation, ed. Del Grosso, S. J., Ahuja, L., and Parton, W. J.. Madison, WI: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 21–58.Google Scholar
Grant, R. F. (2001). A review of the Canadian ecosystem model ecosys. In Modeling Carbon and Nitrogen Dynamics for Soil Management, ed. Shaffer, M. J.. Boca Raton, FL: CRC Press, 173–264.Google Scholar
Griffin, W. A. (2006). Agent-based modeling for the theoretical biologist. Biological Theory, 1(4), 404–9.Google Scholar
Hall, D. O., Ojima, D. S., Parton, W. J., and Scurlock, J. M. O. (1995). Response of temperate and tropical grasslands to CO2 and climate change. Journal of Biogeography, 22, 537–47.Google Scholar
Hilbers, J. P., Van Langevelde, F., Prins, H. H. T., et al. (2015). Modeling elephant-mediated cascading effects of water point closure. Ecological Applications, 25, 402–15.Google Scholar
Hobbs, N. T., Baker, D. L., Ellis, J. E., and Swift, D. M. (1981). Composition and quality of elk winter diets in Colorado. Journal of Wildlife Management, 45, 156–71.Google Scholar
Hobbs, N. T., Geremia, C., Treanor, J., et al. (2015). State-space modeling to support management of brucellosis in the Yellowstone bison population. Ecological Monographs, 85(4), 525–56.Google Scholar
Hudiburg, T. W., Wang, W., Khanna, M., et al. (2016). Impacts of a 32-billion-gallon bioenergy landscape on land and fossil fuel use in the US. Nature Energy, 1, Article 15005.Google Scholar
Hunt, H. W., Coleman, D. C., Ingham, E. R., et al. (1987). The detrital food web in a shortgrass prairie. Biology and Fertility of Soils, 3(1), 57–68.Google Scholar
Hunt, H. W., and Parton, W. J. (1986). The role of mathematical models in research on microfloral and faunal interactions in natural and agroecosystems. In Microfloral and Faunal Interactions in Natural and Agroecosystems, ed. Mitchell, M. J. and Nakas, J. P.. Dordrecht: M. Nyhoff/Dr. W. Junk Publishers, 443–94.Google Scholar
Hunt, H. W., Trlica, M. J., Redente, E. F., et al. (1991). Simulation model for the effects of climate change on temperate grassland ecosystems. Ecological Modelling, 53, 205–46.Google Scholar
Huston, M., Deangleis, D., and Post, W. (1988). New computer-models unify ecological theory-computer-simulations show that many ecological patterns can be explained by interactions among individual organisms. Bioscience, 38(10), 682–91.Google Scholar
Innis, G. S., ed. (1978). Grassland Simulation Model. Ecological Studies, 26. New York: Springer.Google Scholar
Kelly, R. H., Parton, W. J., Crocker, G. J., et al. (1997). Simulating trends in soil organic carbon in long-term experiments using the Century model. Geoderma, 81, 75–90.Google Scholar
Kim, Y., Talucder, M. S. A., Kang, M., et al. (2016). Interannual variations in methane emission from an irrigated rice paddy caused by rainfalls during the aeration period. Agriculture, Ecosystems & Environment, 223, 67–75.Google Scholar
Kirchner, T. B. and Whicker, F. W. (1983/1984). Validation of PATHWAY, a simulation model of the transport of radionuclides through agroecosystems. Ecological Modeling, 22, 21–44.Google Scholar
Kittel, T. G. F., Ojima, D. S., Schimel, D. S., et al. (1996). Model GIS integration and data set development to assess terrestrial ecosystem vulnerability to climate change. In GIS and Environmental Modeling: Progress and Research Issues. Canada: John Wiley and Sons, 293–7.Google Scholar
LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M. C. (2013). Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs, 83(2), 133–54.Google Scholar
Lesorogol, C. K., and Boone, R. B. (2016). Which way forward? Using simulation models and ethnography to understand changing livelihoods among Kenyan pastoralists in a “new commons.” International Journal of the Commons, 10, 747–70.Google Scholar
Li, C. (1996). The DNDC model. In Evaluation of Soil Organic Matter Models Using Existing, Long-Term Datasets, NATO ASI Series I, vol. 38, ed. Powlson, D. S., Smith, P., and Smith, J. U.. Heidelberg: Springer, 263–7.Google Scholar
Liedloff, A. C., Coughenour, M. B., Ludwig, J. A., and Dyer, R. (2001). Modelling the trade-off between fire and grazing in a tropical savanna landscape, northern Australia. Environmental International, 27(2–3), 173–80.CrossRefGoogle Scholar
Lorenz, T. (2009). Epistemological aspects of computer simulation in the social sciences. Lecture Notes in Computer Science, 5466, 141–52.Google Scholar
Malone, S. L., Keough, C., Staudhammer, C. L., et al. (2015). Ecosystem resistance in the face of climate change: A case study from the freshwater marshes of the Florida Everglades. Ecosphere, 6(4), Article 57.Google Scholar
May, R. M. (1973). Qualitative stability in model ecosystems. Ecology, 54(3), 638–41.Google Scholar
McGill, W. B., Hunt, H. W., Woodmansee, R. G., and Reuss, J. O. (1981). Phoenix, a Model of the Dynamics of Carbon and Nitrogen in Grassland Soils. Ecological Bulletin, 33. Stockholm: Swedish Natural Science Research Council, 49–115 .Google Scholar
Melillo, J. M., Frey, S. D., DeAngelis, K. M., et al. (2017). Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science, 358(6359), 101–5.Google Scholar
Metherell, A. K., Cambardella, C. A., Parton, W. J., et al. (1995). Simulation of soil organic matter dynamics in dryland wheat-fallow cropping systems. In Soil Management and Greenhouse Effect, ed. Lal, R., Kimball, J., Levine, E., and Stewart, B. A.. Boca Raton, FL: CRC Press, 259–70.Google Scholar
Migliorati, M. D. A., Parton, W. J., Del Grosso, S. J., et al. (2015). Legumes or nitrification inhibitors to reduce N2O emissions in subtropical cereal cropping systems? A simulation study. Agriculture, Ecosystems and Environment, 213, 228–40.Google Scholar
Moore, J. C., Berlow, E. L., Coleman, D. C., et al. (2004). Detritus, trophic dynamics and biodiversity. Ecology Letters, 7(7), 584–600.Google Scholar
Moore, J. C., and de Ruiter, P. C. (2012). Models of simple and complex systems. In Energetic Food Webs: An Analysis of Real and Model Ecosystems. New York: Oxford University Press, 27–53.Google Scholar
Moore, J. C., de Ruiter, P. C., and Hunt, H. W. (1993). Influence of productivity on the stability of real and model-ecosystems. Science, 261(5123), 906–8.Google Scholar
Moore, J. C., McCann, K., Setala, H., and de Ruiter, P. C. (2003). Top-down is bottom-up: Does predation in the rhizosphere regulate aboveground dynamics? Ecology, 84(4), 846–57.Google Scholar
Mosier, A., Schimel, D. S., Valentine, D., Bronson, K., and Parton, W. J. (1991). Methane and nitrous oxide fluxes in native, fertilized and cultivated grasslands. Nature, 350, 330–2.Google Scholar
Moore, J. C., Walter, D. E., and Hunt, H. W. (1988). Arthropod regulations of microbiota and meso biota in belowground detrital food webs. Annual Review of Entomology, 33, 419–39.Google Scholar
Necpálová, M., Anex, R. P., Fienen, M. N., et al. (2015). Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling. Environmental Modelling & Software, 66, 110–30.Google Scholar
Ojima, D. S., Schimel, D. S., Parton, W. J., and Owensby, C. (1994). Long- and short-term effects of fire on N cycling in tallgrass prairie. Biogeochemistry, 24, 67–84.Google Scholar
Pan, Y., Melillo, J. M., McGuire, A. D., et al. (1998). Modeled responses of terrestrial ecosystems to elevated atmospheric CO2: A comparison of simulations by the biogeochemistry models of the vegetation/ecosystem modeling and analysis project (VEMAP). Oecologia, 114, 389–404.Google Scholar
Parton, W. J., Coughenour, M. B., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G., Scholes, R. J., Schimel, D. S., Kirchner, T. B., Menaut, J. C., Seasteadt, T., Garcia-Moya, E., Kamnalrut, A., Kinyamario, J. I., and Hall, D. O. (1996). Global grassland ecosystem modeling: Development and test of ecosystem models for grassland systems. In Global Change: Effects on Coniferous Forests and Grasslands, ed. Breymeyer, A. I., Hall, D. M., Melillo, J. M., and Agren, G. I., SCOPE. Hoboken, NJ: John Wiley and Sons Ltd., 229–66.Google Scholar
Parton, W. J., Gutmann, M. P., Merchant, E. R., et al. (2015). Measuring and mitigating agricultural greenhouse gas production in the US Great Plains, 1870–2000. Proceedings of the National Academy of Sciences of the United States of America, 112(34): E4681–E4688.Google Scholar
Parton, W. J., Hartman, M., Ojima, D., and Schimel, D. (1998). DAYCENT and its land surface submodel: Description and testing. Global and Planetary Change, 19, 35–48.Google Scholar
Parton, W. J., Holland, E. A., Del Grosso, S. J., et al. (2001). Generalized model for NOx and N2O emissions from soils. Journal of Geophysical Research-Atmospheres, 106, 17403–20.Google Scholar
Parton, W. J., Neff, J. and Vitousek, P. M. (2005). Modelling phosphorus, carbon and nitrogen dynamics in terrestrial ecosystems. In Organic Phosphorus in the Environment, ed. Turner, B. L., Frossard, E., and Baldwin, D. S.. CAB International, 325–44.Google Scholar
Parton, W. J., and Rasmussen, P. E. (1994). Long-term effects of crop management in a wheat/fallow system: II. Modelling change with the CENTURY model. Soil Science Society of America Journal, 58, 530–6.CrossRefGoogle Scholar
Parton, W. J., and Risser, P. G. (1979). Simulating impact of management practices upon the tallgrass prairie. In Perspectives in Grassland Ecology, ed. French, N. R.. New York: Springer Verlag, 135–56.Google Scholar
Parton, W. J., and Risser, P. G. (1980). Impact of management practices on the tallgrass prairie. Oecologia, 46(2), 223–34.Google Scholar
Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. (1987). Analysis of factors controlling soil organic levels of grasslands in the Great Plains. Soil Science Society of America Journal, 51, 1173–9.Google Scholar
Parton, W. J., Schimel, D. S., and Ojima, D. S. (1994). Environmental change in grasslands: Assessment using models. Climatic Change, 28, 111–41.CrossRefGoogle Scholar
Parton, W. J., Scurlock, J. M. O., Ojima, D. S., et al. (1995). Impact of climate change on grassland production and soil carbon worldwide. Global Change Biology, 1, 13–22.Google Scholar
Parton, W., Silver, W. L., Burke, I. C., et al. (2007). Global-scale similarities in nitrogen release patterns during long-term decomposition. Science, 315(5810), 361–64.Google Scholar
Parton, W. J., Singh, J. S., and Coleman, D. C. (1978). A model of production and turnover of roots in shortgrass prairie. Journal of Applied Ecology, 47, 515–42.Google Scholar
Parton, W. J., Stewart, J. W. B., and Cole, C. V. (1988). Dynamics of C, N, P, and S in grassland soils: A model. Biogeochemistry, 5, 109–31.Google Scholar
Peinetti, H. R., Menezes, R. S. C., and Coughenour, M. B. (2001). Changes induced by elk browsing in the aboveground biomass production and distribution of willow (Salix monticola Bebb): Their relationships with plant water, carbon, and nitrogen dynamics. Oecologia, 127(3), 334–42.Google Scholar
Plumb, G. E., White, P. J., Coughenour, M. B., and Wallen, R. L. (2009). Carrying capacity and migration of Yellowstone bison: Implications for conservation. Biological Conservation, 142, 2377–87.Google Scholar
Pries, C. E. H., Castanha, C., Porras, R. C., and Torn, M. S. (2017). The whole-soil carbon fluxin response to warming. Science, 355(6332), 1420–2.Google Scholar
Probert, M. E., Keating, B. A., Thompson, J. P., and Parton, W. J. (1995). Modelling water, nitrogen, and crop yield for a long-term fallow management experiment. Australian Journal of Experimental Agriculture, 35, 941–50.Google Scholar
Rafique, R., Fienen, M. N., Parkin, T. B., and Anex, R. P. (2013). Nitrous oxide emissions from cropland: A procedure for calibrating the DayCent biogeochemical model using inverse modelling. Water, Air, & Soil Pollution, 224(9), Article 1677.Google Scholar
Rafique, R., Kumar, S., Luo, Y., et al. (2014). Estimation of greenhouse gases (N2O, CH4 and CO2) from no-till cropland under increased temperature and altered precipitation regime: A DAYCENT model approach. Global and Planetary Change, 118, 106–14.Google Scholar
Rooney, N., McCann, K., Gellner, G., and Moore, J. C. (2006). Structural asymmetry and the stability of diverse food webs. Nature, 442(7100), 265–9.Google Scholar
Sala, O. E., Parton, W. J., Joyce, L. A., and Lauenroth, W. K. (1988). Primary production of the Central Grassland Region of the United States. Ecology, 69(1), 40–5.Google Scholar
Savage, K. E., Parton, W. J., Davidson, E. A., Trumbore, S. E., and Frey, S. D. (2013). Long-term changes in forest carbon under temperature and nitrogen amendments in a temperate northern hardwood forest. Global Change Biology, 19(8), 2389–400.Google Scholar
Scheer, C., Del Grosso, S. J., Parton, W. J., Rowlings, D. W., and Grace, P. R. (2014). Modeling nitrous oxide emissions from irrigated agriculture: Testing DayCent with high‐frequency measurements. Ecological Applications, 24(3), 528–38.Google Scholar
Schimel, D. S., Braswell, B. H., McKeown, R., et al. (1996). Climate and nitrogen controls on the geography and timescales of terrestrial biogeochemical cycling. Global Biogeochemical Cycles, 10, 677–92.Google Scholar
Schimel, D. S., Braswell, B. H., and Parton, W. J. (1997a). Equilibrium of the terrestrial water, nitrogen, and carbon cycles. Proceedings of the National Academy of Sciences of the United States of America, 94(16), 8280–3.Google Scholar
Schimel, D. S., Coleman, D. C., and Horton, K. A. (1985). Soil organic-matter dynamics in paired rangeland and cropland topsequences in North-Dakota. Geoderma, 36(3–4), 201–14.Google Scholar
Schimel, D. S., Emanuel, W., Rizzo, B., et al. (1997b). Continental scale variability in ecosystem processes: Models, data, and the role of disturbance. Ecological Monographs, 67(2), 251–71.Google Scholar
Schimel, D. S., Kittel, T. G. F., and Parton, W. J. (1991). Terrestrial biogeochemistry cycles: Global interactions with the atmosphere and hydrology. Tellus, 43AB, 188–203.Google Scholar
Schimel, D. S., Melillo, J. M., Tian, H., et al. (2000). Contribution of increasing CO2 and climate to carbon storage by ecosystems in the United States. Science, 287(5460), 2004–6.Google Scholar
Smith, P., Smith, J. U., Powlson, D. S., et al. (1997). A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma, 81, 153–225.Google Scholar
Soong, J. L., Parton, W. J., Calderon, F., Campbell, E. E., and Cotrufo, M. F. (2015). A new conceptual model on the fate and controls of fresh and pyrolized plant litter decomposition. Biogeochemistry, 124, 27–44.Google Scholar
Straube, J. R., Chen, M., Parton, W. J., et al. (2018). Development of the DayCent-Photo model and integration of variable photosynthetic capacity. Frontiers of Earth Science, 12(4), 765–78.Google Scholar
Sun, H., Zhou, S., Fu, Z., et al. (2016). A two-year field measurement of methane and nitrous oxide fluxes from rice paddies under contrasting climate conditions. Scientific Reports, 6.Google Scholar
Swift, D. M. (1983). A simulation-model of energy and nitrogen-balance for free-ranging ruminants. Journal of Wildlife Management, 47(3), 620–45.Google Scholar
VEMAP, et al., Melillo, J. M., Borchers, J., et al. (1995). Vegetation/ecosystem modeling and analysis project: Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Global Biogeochemical Cycles, 9, 407–37.Google Scholar
Wagner-Riddle, C., Congreves, K. A., Abalos, D., et al. (2017). Globally important nitrous oxide emissions from croplands induced by freeze-thaw cycles. Nature Geoscience, 10(4), 279–83.Google Scholar
Wagner-Riddle, C., Hu, Q. C., Van Bochove, E., and Jayasundara, S. (2008). Linking nitrous oxide flux during spring thaw to nitrate denitrification in the soil profile. Soil Science Society of America Journal, 72(4), 908–16.Google Scholar
Weisberg, P. J., and Coughenour, M. B. (2003). Model-based assessment of aspen responses to elk herbivory in Rocky Mountain National Park, USA. Environmental Management, 32(1), 152–69.Google Scholar
Wiens, J. A., and Innis, G. S. (1974). Estimation of energy flow in bird communities: A population energetics model. Ecology, 55(4), 730–46.Google Scholar
Woodmansee, R. G. (1978). Critique and analyses of the grassland ecosystem model ELM. In Grassland Simulation Model, ed. Innis, G. S.. New York: Springer Verlag.Google Scholar
Wu, X., and Zhang, A. (2014). Comparison of three models for simulating N2O emissions from paddy fields under water-saving irrigation. Atmospheric Environment, 98, 500–9.Google Scholar
Xiao, X., Ojima, D. S., Parton, W. J., Zuozhong, C., and Du, C. (1995). Sensitivity of Inner Mongolia grasslands to global climate change. Journal of Biogeography, 22, 643–8.Google Scholar
Yeluripati, J. B., van Oijen, M., Wattenbach, M., et al. (2009). Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models. Soil Biology and Biochemistry, 41(12), 2579–2583.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×