Modeling the Biogeochemistry of Nutrients Flow into Ground and Surface Waters and Air from Agroecosystems R. César Izaurralde Joint Global Change Research Institute Pacific Northwest Nat l Lab. and Univ. of Maryland College Park, MD September 16-17, 2013 A Workshop on Mapping the Food System and its Effects The Keck Center of the National Academies 500 Fifth St., NW Washington, DC
Acknowledgements Sponsors C.D. Jones Colleagues Joint Global Change Research Institute, PNNL & UMD X. Zhang Joint Global Change Research Institute, PNNL & UMD DOE Office of Science Integrated Assessment Program DOE Office of Science Great Lakes Bioenergy Research Center DOE EERE Office of Biomass Program Great Lakes Bioenergy Research Center USDA NIFA Univ. Wisconsin Dairy Coordinate Ag Project
Talk outline as suggested by the committee s questions Options and status of models for assessing nutrient flows and water quality Types of biogeochemical models: Crop production, including nutrient flow and pesticide fate? Livestock production, including feed rations, manure production, and environmental fate? What is the role of biogeochemical models in a framework for assessing impacts of the U.S. farm and food system? What criteria should be used to select biogeochemical simulation models? Are there simpler alternatives to the use of (complex) biogeochemical models? What environmental outcomes are not currently captured by biogeochemical models?
Some definitions Agroecosystem model: A mathematical representation of biophysical, biogeochemical, and human processes and their interactions that occur in a terrestrial ecosystem associated with food, feed, fiber, and biofuel production Weather Landscape and soil properties Hydrological cycle Plant growth and development Nutrient cycling (e.g. N, P, etc.) Environment control (e.g. crop type and sequence, grazing, tillage, irrigation, fertilization, etc.) Biogeochemical cycles: the pathway of elements or molecules through biotic and abiotic compartment of Earth Water Carbon Nitrogen Phosphorus Sulfur Altered biogeochemical cycles
A biogeochemical cycle: The N cycle Galloway et al. (2003) BioScience 53:341-356.
How many models out there? Scores of agroecosystem models developed over the last four decades worldwide Example I (right panel): 27 crop simulation models were tested within the Agricultural Model Intercomparison and Improvement Project (www.agmip.org) against wheat field data from four contrasting environments using standardized protocols (Asseng et al. 2013) Example II: 21 terrestrial ecosystem models were tested within NACP (North American Carbon Program) in their ability to simulate net ecosystem exchange against data from 44 eddy covariance forest and agricultural sites (Schwalm et al. 2010. J. Geophys. Res. 115) Asseng et al. 2013. Nature Climate Change 3:827-832.
Types of models Models built to answer different questions; i.e. emphasis on different processes Crop vs. soil productivity: DSSAT vs. EPIC Crop vs. livestock; mixed systems Most models operate at daily time step but differ in their spatial scale Field scale: e.g. APSIM, CropSyst, DayCent, DNDC, DSSAT, EPIC, SALUS Farm scale: APEX, IFSM Watershed scale: APEX, SWAT All models are process-based but differ in levels of process-representation Representation of plant growth: photosynthesis / respiration vs. radiation use efficiency Soil organic matter: most models based on Century concepts and equations Models benefit from existing models and contribute to new ones Gassman et al. (2005). Historical development and applications of the EPIC and APEX model. CARD Working Paper 05- WP 397. Iowa St. Univ. Forest-based models have been adding agricultural detail to model land-use change: e.g. Agro-IBIS, Agro-BGC
Examples of biogeochemical modeling Greenhouse gases
Capturing temporal distribution of N 2 O fluxes with the DNDC model DNDC-modeled (lines) and measured (symbols) N 2 O emissions from soils under reduced tillage in Italy (Lugato et al. 2011. Agric. Ecosys. Environ. 139:546 556) Aircraft-based measured and DNDC modeled N 2 O emissions in Canada (Desjardins et al. 2010. Agric. For. Meteor. 150:817 824). DNDC-modeled (lines) and measured (symbols) N 2 O emissions from soils under conventional tillage in Germany (Ludwig et al. 2011. Soil Tillage Res. 112:114 121)
Modeling N 2 O and N 2 production generated from denitrification and nitrification process Parton et al. 1996. Global Biogeochem. Cycles 10:401-412 Daily time step process-based model developed on the basis of the Century model Models nitrification as a function of soil ph, soil water content, soil temperature, and soil NH + 4 level N 2 O formation during nitrification is a direct function of nitrification rate N 2 O and N 2 formation during denitrification is modeled as a function of heterotrophic respiration, soil NO - 3 level, and water-filled pore space (WFPS) IPCC estimated and DNDC modeled comparisons against observed N 2 O emissions in 5 US states and 1 Canadian province (Del Grosso et al. 2005. Soil Tillage Res. 83:9 24). Parton et al. 1996. Global Biogeochem. Cycles 10:401-412. 10
Examples of biogeochemical modeling (cont d) Air quality
Linking EPIC with a regional air-quality model (CMAQ) to simulate continental-scale NH 3 fluxes Excess N in the environment can negatively affect aquatic and terrestrial ecosystems Atmospheric NH 3 neutralizes atmospheric (sulfuric and nitric) acids to form NH 4 + aerosols, a major constituent of PM 2.5 Integrated modeling methodology facilitated assessment of soil-nh 3 emissions as affected by fertilizermanagement conditions Coupling EPIC with CMAQ Cooter et al. 2012. Biogeosciences 9:4023-4035.
Examples of biogeochemical modeling (cont d) Water quality (nutrients and pesticides)
Water quality modeling with EPIC Three watersheds at the USDA North Appalachian Experimental Watershed near Coshocton, Ohio Management varied over time (1958-2001): corn, soybean, pasture, till, no till EPIC reproduced long-term observed trends in runoff and soil C losses Izaurralde et al. 2007. Climatic Change 50:479-493.
Water quality modeling with APEX Watershed locations Two watersheds at the USDA Deep Loess Research Station near Treynor, Iowa Continuous corn under conventional and ridge tillage APEX simulated beneficial effects of ridge tillage in reducing runoff and soil C losses Wang et al. 2008. Soil Tillage Res. 50:479-493. Predicted vs. observed runoff and sediment yield
Water quality modeling with SWAT Raccoon River watershed (9,400 km 2 ) in west-central Iowa Exports highest NO 3 -N in US Major source of sediment and nutrient loadings SWAT Calibrated and validated for streamflow, sediment losses, and nutrient loadings Used to assess changes in land use and management practices to control pollution Jha et al. 2007. Trans. ASABE 50:479-493.
Whole farm simulation with IFSM IFSM derives from DAFOSYM, a dairy forage system model IFSM simulates all major farm components at process level Crop production Manure handling, tillage, planting, harvesting, etc. Nutrient requirements for dairy of beef herds Nutrient flows to calculate nutrient accumulation in soil or loss to environment Manure storage and handling Greenhouse gas emissions from soil, manure, or animal The Integrated Farm System Model simulates materials and nutrient flows for various farm systems Rotz et al. 2012. IFSM Reference Manual v3.6, USDA-ARS, 191 pp.
Integrated spatial modeling system (data, model, and output) Zhang X. et al. 2010. Global Change Biology Bioenergy 2:258-277. Nichols J. et al. 2011. Computers Electronics Agriculture 79:112-115.
Biomass, bioenergy, and environmental modeling DOE Great Lakes Bioenergy Research Center Annual residues come at lower biomass prices, then perennial grasses Intensive Modeling Area in Michigan Environmental impacts jump with annual residue harvest, but drop with perennial biomass crops Egbendewe-Mondzozo A. et al. 2011. Biomass Bioenergy 35:4636-4647. Egbendewe-Mondzozo A. et al. 2013. Energy Policy 57:518-531.
Integrating landscape diversity estimation into the EPIC model Modify pest factor in EPIC to account for impact of soybean aphid on yield as modified by landscape biodiversity Simulated soybean yield differences when aphid infestations occur Sahajpal et al. 2011. Ecol. Soc. Am. 2011 Meeting, Austin, TX.
USDA NRCS: National Assessment of Croplands Objective: to estimate the environmental benefits and effects of conservation practices applied to cultivated cropland and cropland enrolled in long-term conserving cover Assessment uses sampling and modeling approach to estimate benefits of conservation practices Models: APEX: Field scale modeling Erosion N & P losses Pesticide fate SWAT: Watershed modeling Transport of water and pollutants from land to receiving streams and down to eventually to estuaries and oceans River Basin Cropland Modeling Study Reports; e.g: Upper Mississippi River Basin Chesapeake Bay Nested modeling system SWAT APEX Water Resource Regions for CEAP
Summary Options and status of models for assessing nutrient flows and water quality Good number of models available for assessment of nutrient flows and water quality Representation of biogeochemical processes exist at different levels of complexity What is the role of biogeochemical models in a framework for assessing impacts of the U.S. farm and food system? Essential role for assessing impacts of farm activities on food production as well as ecosystem and human health What criteria should be used to select biogeochemical simulation models? Documentation Validation Uncertainty assessment Feasibility of multi-model approach Are there simpler alternatives to the use of (complex) biogeochemical models? Metamodels could be good option What environmental outcomes are not currently captured by biogeochemical models? Biodiversity Human and ecosystem health
Abbreviated and descriptive model names, and a key reference Abbreviated model name Descriptive model name References APSIM APEX Agricultural Production System Simulator Agricultural Productivity Environmental Extender Keating et al. 2004. Europ. J. Agron. 18:267-288. Williams and Izaurralde. 2006. The APEX model. Taylor & Francis Group, Boca Raton, FL. CropSyst Cropping Systems Simulator Stockle et al. 2003. Eur. J. Agron. 18:298-307. DayCent Daily Century Parton et al. 1996. Global Biogeochem. Cycles 10:401 412. DNDC DeNitrification DeComposition Li et al. 1992. J. Geophys. Res. Atmos. 97:9759-9776. DSSAT EPIC Decision Support System for Agrotechnology Transfer Environmental Policy Integrated Climate Jones et al. 2003. Eur. J. Agron. 18:235-265. Williams et al. 1984. Trans. ASAE 27:129-144. IFSM Integrated Farm System Model Rotz et al. 2012. IFSM Reference Manual v3.6, USDA-ARS, 191 pp. SALUS Systems Approach to Land Use Sustainability Basso et al. 2006. Int. J. Agron. 4:677 688. SWAT Soil and Water Assessment Tool Arnold et al. 1998. J. Am. Water Resour. Assoc. 34:73-89.