Nitrous Oxide Emissions from Biofuel Crops and Parameterization in the EPIC Biogeochemical Model Priya Pillai and Viney P. Aneja North Carolina State University Raleigh, NC Aleksandra Njegovan, John T. Walker, Ellen Cooter, and D. Kirchgessner U.S. Environmental Protection Agency Research Triangle Park, NC American Chemical Society National Meeting, Philadelphia, PA August 19-23, 2012.

Outline  Introduction and Motivation  Current Study Objective  Experimental Design  Measurements  Results and Discussion  Summary and Conclusions  Future Directions

 Fossil fuels: Are a nonrenewable energy source Emit greenhouse gases and perturb earth’s climate (IPPC 2007)  The U.S. Renewable Fuels Standard mandates a 36 billion gallon use of renewable transportation fuels by 2012  Replacing fossil fuels by biofuels decreases atmospheric global warming potential (GWP) by reducing net CO 2 emissions. This may partially offset GWP attributed to soil Nitrous Oxide (N 2 O) emission from fertilizer application to biofuel crops. Introduction For the highest yield for biofuels, an optimal management plan is critical

Production includes electricity, heat, and transportation fuels Renewable Energy Sources

U.S. Energy Information Administration: Annual Energy Review 2010  8% renewable: 48% biomass and 23% biofuels Renewable Energy as Share of Total Primary Energy Consumption

 Biofuels are the only renewable energy source that produces transportation fuels in commercial quantities.  Agricultural activities contribute approximately 78% of global anthropogenic N 2 O of which 67% comes from agricultural soils.  N 2 O is one of the most important trace gases in the atmosphere owing to its radiative properties and role in stratospheric ozone depletion.  N 2 O emissions are influenced by the rate, timing, type, and method of nitrogen fertilization in biofuel crop production. Introduction….

 Environmental benefits of biofuels Carbon sequestration and GHG mitigation  Forest health Reduce wildfire risk Recovery of degraded land  Economic benefits Increased income potential Economic diversification  Societal benefits Renewable and improved energy security Motivation Challenges include education, collaborations, market development

 Assess N 2 O emissions from switch grass (SG) and corn as a function of nitrogen (N) application rate.  Examine relationship between SG yield and N application rate to estimate an optimal N application for the highest yield and lowest N 2 O emissions.  Use field observations and site characteristics to parameterize the EPIC model for simulation of N 2 O emissions.  Data presented represent results from the 1 st year of a 3 year project. For the highest yield for biofuels, an optimal management plan is critical Objectives

 Corn variety is DeKalb C6805 planted on May 5, 2011 at 28,000 seeds per acre.  25% of fertilizer treatment was applied at planting and 75% was applied when corn reached a height of approximately 30’’.  Alamo variety SG was planted in December 2008 at 9.2 lbs seed per acre.  SG is cut annually and fertilized once in the Spring. Fertilizer is ammonium sulfate granules and applied by hand Experimental Design

 Corn and SG plots (6.7’ x 15’) are configured as random blocks with 4 permanent soil collars located randomly in each plot.  SG treatments consist of three N application rates (60, 120, and 180 kg N ha -1 yr -1 applied) and a control treatment (0 N), with 4 replicate plots of each treatment.  Corn treatments consist of three N application rates (60, 120, and 180 kg N ha -1 yr -1 ) with 4 replicate plots of each treatment. Experimental Design Measurement site is Holly Springs, North Carolina

 N 2 O fluxes are measured by vented static chambers.  PVC collars are permanently driven into the ground to facilitate a gas tight seal with the overlying PVC chamber (314 cm 2 ).  Chamber is inserted over the soil collar and 15 mL gas samples are withdrawn at 10, 20, and 30 minutes.  N 2 O fluxes are calculated from the slope of the increase in N 2 O concentration in the chamber between 0 to 30 minutes.  Fluxes are corrected for chamber effects following recommendations of Venterea (2010). Measurements

Chamber Frame base Needle Rubber Stopper Frame base Schematic of Flux Chamber

 Major Processes Weather Hydrology Soil temperature, moisture Erosion-sedimentation Nutrient and C cycling Plant Growth Farming operations Management EPICis programmed in FORTRAN and runs on daily time step Erosion Productivity Impact Calculator (EPIC) Model

 Major Inputs Site characteristics Monthly and daily weather Soil properties Land use or farming operations Nutrient and C cycling Plant Growth Management Major outputs include crop yield and nitrogen losses as a function of management activity Erosion Productivity Impact Calculator (EPIC) Model

 SITE*.DAT latitude, longitude, slope, elevation, etc.  WPMO*.DAT, Daily weather File,WINDMO*.DAT Monthly weather (generator), daily weather (obs. /sim.), wind  SOIL*.DAT texture, bulk density, pH  OPSC*.DAT Crop type, planting, harvesting, tillage operation, fertilization, irrigation. Must be input in chronological order.  EPICCONT.DAT Number of years, simulation start year, weather options, etc. Erosion Productivity Impact Calculator (EPIC) Model Input Files

N 2 O Flux (ng N m -2 s -1 ) as function of N application rate (TRT) Results Corn: Emission of N 2 O dependent on N application rate SG: No strong dependency during growing season

Results N 2 O emission from SG and Corn For each treatments Lowest average N 2 O flux was observed for TRT2 in SG (0.49) and the flux increased ~ 2times for TRT3 in SG

Crop Height

Crop Yield Though large within treatment variability was observed, mean yields increased with increasing nitrogen application rates in both SG and Corn.

Comparison between observation and simulation CORN YieldSimulated tons/ haObserved tons/ ha TRT TRT TRT For the variety of corn selected, the crop height is limited in EPIC to 2.0 m. A crop height of 2.0 m at the time of harvest was predicted by EPIC for all treatments.

Comparison between observation and simulation SG YieldSimulated (tons/ha)Observed (tons/ha) TRT TRT TRT TRT For the variety of Switch Grass selected, the crop height is limited in the model to 2.5 m. Model predicted 2.5 m crop height for treatments at the time of harvest.

EPIC Predicted Monthly Crop Height The measured crop height at harvest is comparable to the EPIC simulation.

Observed N 2 O and EPIC Predicted N 2 O For SG, N 2 O emission from denitrification for all fertilizer treatments was ~ 0.13 ng N ha -1 during May when the fertilizer was applied and decreased to near zero during the growing season. As fertilization amount increases, the model predicted N 2 O emission increases for corn. SepAugJulJuneMay

Observed vs EPIC Predicted N 2 O Denitrification rate is estimated using an exponential function of temperature, organic carbon, and NO 3 -N concentration and is allowed to occur in EPIC only when soil water content is ≥ 95 % of saturation. EPIC simulates the transformation of NH 4 + to NO 3 + through nitrification. Denitrification of NO 3 + produces N 2 O and organic N undergoes mineralization. For treatments 1 and 2, N 2 O fluxes predicted by EPIC compare reasonably well with observations. However, EPIC could not completely capture the peak N 2 O emissions (Jul and Aug) from high N fertilized plots, possibly due to the daily time step of the model.

Summary & Conclusions  This study attempts to identify optimal fertilizer application rate for highest yield and lowest N 2 O emission from biofuel crops such as switch grass.  During the first year of this study, SG and corn yields were observed to increase with amount of nitrogen fertilizer applied. This pattern was successfully simulated by the EPIC model.  EPIC also predicted a positive relationship between amount of nitrogen applied to corn and amount of N 2 O produced, which is consistent with the pattern of treatment differences in fluxes measured in corn.  In the selected N application rate cases, the N 2 O emission is lower for SG compared to Corn.

Summary & Conclusions  N fertilizer application rate of 120 kg N ha -1 yr -1 corresponded to highest crop yield with lowest N 2 O emission. Additional data are being collected to assess consistency of this result during years 2 and 3 of the project.  The changes in soil chemistry and nutrient cycling induced by increased N fertilization increase the denitrification rate and thus the nitrous oxide flux in the case of corn.  However, the model could not completely capture the peak N 2 O emissions from highest N fertilized plots, possibly because N 2 O production via nitrification is not simulated in EPIC.  Current soil structure information provided as an input to EPIC may not completely represent the desired land management scenario.

Future Work  Additional data are being collected to more closely examine the relationships between N 2 O fluxes, nitrogen application rate, soil moisture, and temperature in SG in order to better understand the performance of the EPIC model.  Detailed sensitivity analyses will be conducted to understand the peak nitrous oxide emission scenario.  The model may run for a longer spin-up period to allow nutrient pools and soil characteristics to adjust to the defined management environment. Thank You

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