Use of Models to Simulate Agricultural Greenhouse Gas Emissions Bill Parton Steve Del Grosso
My Biases 1. Computer Models are the main tool for estimating Greenhouse Gas (N 2 O, CH 4, CO 2 ) emissions from ag soils 2. Expensive to measure Greenhouse Gas fluxes in the field 3. Multiple model Greenhouse Gas flux estimates are needed
Outline 1. DayCent model description 2. Model validation/testing 3. Improved management options Fertilization timing and amounts Cover crops Slow release fertilization and nitrification inhibitors 4. Biofuel options Net Greenhouse Gas fluxes 5. Summary
WATER FLOW SUBMODEL ATMOS PRECIP ATMOS T air SNOW PET Sublimation Evap PET BIOMASS INTERCEPTION T air PET ROOT PET Infiltration K SAT Melting H 2 O -1 cm K SAT K SAT h pot K SAT H 2 O 1-4 cm H 2 O 4-15 cm h pot OUTFLOW K SAT h pot H 2 O 15-3 cm K SAT h pot H 2 O 145-16 cm K SAT
Model Testing/Validation
Ontario corn 7 6 5 4 3 2 1 MODEL TESTING Mean N2O Emissions measured DAYCENT MI forest CO dryland wheat NE dryland wheat MI corn/soy/alfalfa TN corn CO irrigated corn CO irrigated corn/barley CO short grass NE short grass gn ha -1 d -1
CO Irrigated Corn 14 N 2 O Emissios: Corn Hi-N, Conventional-Till gn ha -1 d -1 12 1 8 6 4 2 simulated observed 3/11/5 9/1/5 3/12/6 9/11/6 14 N 2 O Emissios: Corn Hi N, No-Till gn ha -1 d -1 12 1 8 6 4 2 simulated observed 3/11/5 9/1/5 3/12/6 9/11/6
16 12 ww/fal NE - N 2 O emissions observed sim gn ha -1 d -1 8 4 1/31/93 8/2/93 2/1/94 8/3/94 2/2/95 8/4/95 16 grass NE - N 2 O emissions 12 observed sim gn ha -1 d -1 8 4 1/31/93 8/2/93 2/1/94 8/3/94 2/2/95 8/4/95
8 Simulated vs. Observed N 2 O Emissions sim N 2 O (gnha-1 d-1) 7 6 5 4 3 2 y =.9883x +.1488 R 2 =.8639 1 2 4 6 8 obs N 2 O (gn ha-1 d-1)
simualted CH 4 gc ha -1 d -1 3 2 1 intensive ag dryland ag short grass coniferous decidous tropical CH 4 Validaions 1 2 3 observed CH 4 gc ha -1 d -1 ln(simualted N 2O - gn ha -1 d -1 ) 4 3 2 1-1 deciduous forest dryland ag grassland intensive ag organic ag N 2 O Validations -1 1 2 3 4 ln(observed N 2 O - gn ha -1 d -1 )
8 DAYCENT vs NASS County Level Yields simulated gc m -2 yr -1 6 4 2 y =.967x + 24.511 R 2 =.719 2 4 6 8 observed gc m -2 yr -1
4 KBS - Grain Yield gc m -2 yr -1 3 2 corn/soy/wheat: r 2 = 88% sim obs 1 g C m -2 4 3 2 1 1989 1991 1993 1995 1997 1999 CEF, ON - Annual Grain Yields daycent obs gc m -2 25 2 15 1 5 1998 1999 2 21 Sterling, CO - Mean Crop Yields ww/f ww/ann corn millet
24 25 26 25 26 23 24 6 5 4 3 2 1 Compare Water Drainage Compare Nitrate Export 1996 1997 1998 1999 2 21 22 year Observed Simulated 1995 1996 1997 1998 1999 2 21 22 23 1993 1994 1994 1995 1993 1992 1992 8 7 6 5 4 3 2 1 year Observed Simulated kg N/ha/yr water yield cm
EPA Regional DayCent Model Results
Estimated Corn Yield from NASS
Improvements in Agricultural Management
Best Agricultural Management Practices 1. Timing and amount of fertilizer application Winter vs. spring application 2. Time release and nitrification inhibitors 3. Winter cover crops
N Addition Simulations for Central Iowa 4 Mean Annual Corn Grain Yield 16 Mean Annual N losses 35 12 g C m -2 3 first 1 years second 1 years g N m -2 8 25 4 first 1 years second 1 years 2 5 1 15 2 25 3 35 N fert g N m -2 5 1 15 2 25 3 35 N fert g N m -2 6% Mean Annual N losses/n fert 5% 4% 3% 2% 1% % first 1 years second 1 years 5 1 15 2 25 3 35 N fert g N m -2
Fertilizer Type and Tillage Intensity Study Colorado Irrigated Corn 25 N2O gn ha -1 d -1 2 15 1 5 27 28 N CT N NT HI N CT urea HI N NT urea HI N CT time release HI N NT time release HI N CT inhibitor 25 N2O gn ha -1 d -1 2 15 1 5 conventional till no till N Hi N urea Hi N time release Hi N inhibitor
How can we improve? Wind to H 2 to NH 3 Local renewable NH 3 Harvest grain and cobs Cobs for gasification UMM - WCROC NRCS Photo gallery USDA Photo center www.cvec.com Manure back to soil NRCS Photo gallery Improved fertilizers: Slow release Nitrification inhibitors Grain for ethanol Dry distillers grain, feed NRCS Photo gallery www.cvec.com
Biofuel Model Results 1. Existing Ag land 2. Conservation Reserve Program Land 3. Natural grassland
8 Cropland to Biofuels g CO2-C eq. m -2 yr -1 ANPP gc m -2 yr -1 *.1 6 4 2-2 delta SOC N2O GHGnet ANPP CT corn/soy corn/soy to NT corn/soy corn/soy to cont. CT corn corn/soy to cont. NT corn corn/soy to switchgrass
8 CRP to Biofuels g CO2-C eq. m -2 yr -1 ANPP gc m -2 yr -1 *.1 6 4 2-2 delta SOC N2O GHGnet ANPP CRP CRP to CT corn/soy CRP to NT corn/soy CRP to switchgrass
8 Prairie to Biofuels g CO2-C eq. m -2 yr -1 ANPP gc m -2 yr -1 *.1 6 4 2-2 -4 delta SOC N2O GHGnet ANPP prairie prairie harvested prairie harvested + N prairie to CT corn/soy prairie to NT corn/soy prairie to switchgrass
Summary 1. We need computer models to assess greenhouse gas fluxes from agriculture DayCent and DNDC 2. Best management practices can greatly reduce agricultural greenhouse gas fluxes Connection to biofuels 3. Need field measurement program to generate data for model testing
Summary 4. Extensive testing of computer models is essential Standard data sets used to test models Model comparisons are needed