DNDC Modeling to Quantify Mitigation Potential N2O from CA Agricultural

Soils…plus a follow up on OpTIS

Will iam A. Salas*, Applied GeoSolutions, LLC

Jia Deng (Changsheng Li) , University of New Hampshire

Lei Guo, Research Division, ARB

March 8, 2017; C-AGG Meeting in Sacramento, CA

*wsalas@agsemail.com

Collect field data and evaluate DNDC’s processes for modeling N2O emissions in California;Improve the DNDC model through additional model development to support statewide analyses, calibration, and validation;Conduct statistical analysis to assess model structure- and database-induced uncertainty;Create geospatial database and run DNDC to estimate N2O emissions and mitigation potential for California croplandsPerform model analysis of CA N2O emissions (2000-2015) and assess mitigation potential of changes in tillage, cover cropping, nitrification inhibitors and irrigation systems.

Project Objectives

Comparison of the DNDC simulated seasonal or annual N2O emissions against the field measurements for typical cropping systems (alfalfa, wheat, corn, tomato, lettuce, grape vine, cotton, and almond) in California.

Model Validation

Special thanks to UC Davis collaborators.

Input database for modeling N2O emissions from California

County Based Mapping

Meteorological Data (Daymet)

N2O Emission from cropping systems in each county

DNDC ModelSoil properties

(SSURGO)

Crop type (54) and area

Management(fertilization,

irrigation)

Model improvements for regional (statewide) modeling N2O under irrigation methods and baseline simulation

Modified the regional version of DNDC to conduct irrigation-event based simulations – previous version only allowed irrigation index approach which uses crop demand and soil water to set irrigation;Built database to parameterize irrigation practices (water input, irrigation frequency, irrigation depth) for different irrigation methods (surface, sprinkler, drip, or subsurface drip);Conducted simulations under different irrigation methods and calculated baseline N2O by weighting the N2O simulations under irrigation methods with the corresponding fractions for each crop type.

N2O emissions from California croplands

0.50%

0.60%

0.70%

0.80%

0.90%

1.00%

1.10%

1.20%

1.30%

1999 2001 2003 2005 2007 2009 2011 2013 2015

EF

DNDC

ARB Inventory

ARB EF: 1.00%DNDC EF: 1.04% (0.77% to 1.22%)

N2O emissions from N in fertilizers and crop residues

DNDC simulated and ARB reported N2O emissions:1. The N2O emissions estimated by these two methodologies are generally

comparable;2. N2O fluctuations across different years and a decreasing trend.

N2O emissions: inter-annual variations

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

30000

32000

34000

36000

38000

40000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

0

20

40

60

80

100

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

4000

5000

6000

7000

8000

9000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

1.2

1.4

1.6

1.8

2.0

2.2

2.4

R2 = 0.75 n = 15 P < 0.001

R2 = 0.89 n = 15 P < 0.001

Area

(km

2 )

Year

Areaa

0.55

0.59

0.63

0.67

0.71

0.75

N input

N in

put (

Tg N

)

b

Prec

ipita

tion (

cm)

Year

Precipitation

80

85

90

95

100

R2 = 0.84 n = 15 P < 0.001

Irrigation

Irrig

ation

(cm

)

c

N 2O em

issio

ns (M

T N-

N 2O yr

-1)

Year

R2 = 0.72 n = 15 P < 0.001

d

R2 = 0.51 n = 15 P < 0.01

R2 = 0.38 n = 15 P < 0.05

N 2O em

issio

n rate

(kg N

-N2O

ha-1)

Year

0.6

0.8

1.0

1.2

1.4

Em

issio

n fac

tor (

%)

A decreasing trend in N2O emissions has been predicted primarily due to adecreasing in cropland areas and a increasing in low-volume irrigation

N2O emissions from different crop categories

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

0

1500

3000

4500

6000

7500

9000

N 2O em

ission

s (M

T N-

N 2O yr

-1)

Year

Hay Field crops VMB Orchard Vineyard

VMB: Vegetables, Melons, and Berries.

N2O emissions from different counties

Riverside Santa Barbara

Sacramento Sonoma

Butte Solano

San Luis Obispo Sutter Glenn

Colusa Stanislaus

Kings Kern

Imperial Yolo

Monterey Merced Fresno

San Joaquin Tulare

0 2 4 6 8 10 12Percentage of total N2O emissions (%)

County

a b

0

2000

4000

6000

8000

10000

N 2O em

issio

ns (m

etric

ton

N yr

-1) a

Irrigation management

Surface gravity Sprinkler Surface Drip Subsurface drip

8000

N2O emissions under scenarios of irrigation management (a) and from different crop categories (b)in California (using 2002 as an example).The data in grey and slash bars in the plot (b) were calculated by weighting the simulations underthe four irrigation management scenarios with the fractions of corresponding irrigation methods foreach type of crops in 2001 and 2010, respectively.In this way, we have estimated the changes in statewide N2Oemissions due to shifts in irrigationsystems (around 7%) due to changes in irrigation management.

g g

0

1000

20004000

6000

8000b

All cropsVineyardOrchardVMBField crops

N 2O em

issio

ns (m

etric

ton

N yr

-1)

Crop category

N2O mitigation potential: irrigation

VMB: Vegetables, melons, and berries.

RN: reduced N (85%); NI: nitrificationinhibitor (applied in NH4

+-based fertilizers); RT:reduced tillage; NT: no tillage; NLCC: non-leguminous cover crop; LCC: leguminous covercrop; SD: surface drip; SubSD: subsurface drip.The management practices with relative highmitigation potential: RN*, NI, RN+NI*, NLCC,low-volume irrigation.All management impacts of emissions wereevaluated using the 2012 database.

Statewide N2O mitigation: N management, tillage, cover crop, irrigation

0

2000

4000

6000

8000

N2O

Em

issio

ns (M

T N

)

Scenarios

13% 19% 30% 21% 44% 60%

*NB: RN scenario – requires more analysis to understand yield implications.

Soils: performed using the minimum and maximum soil property values as derived from the SSURGO soil database using the approach of the ‘‘Most Sensitive Factor’’ method (Li et al., 2005; Li, 2007).Irrigation water depth: performed by changing the amount of water

applied with +/- 25% of the default value.Scheduling of management practices: performed by changing the

dates of all management events (planting and harvest, irrigation, fertilizer application, and tillage) within a five-week window before and after of the respective default dates.All uncertainty analyses were performed using the 2012 activity and

management database.

Impacts of input uncertainty on simulatedStatewide N2O Emissions:

Parameter Average Minimum (% Average)

Maximum (% Average)

Soil properties 6725 4671 (69.5%) 9190(137%)Irrigation water depth 6725 5638 (83.8%) 7720(115%)

Management practice scheduling

6887 6744 (97.9%) 7066(102%)

Total N2O emissions (Mg N) for 2012 as affected by uncertainties of input parameters*

*Sensitivity Analyses – Single Parameter.

Project Wrap-upUpdate Statewide N 2O emissions and mitigation potential, accounting for a full uncertainty budget

Provide modeling system to ARBTimeline: June 2017

…plus a follow up on OpTIS Indiana Pilot from July 2016 C-AGG meeting (time permitting)

Recall: Applied OpTIS remote sensing system to map crop residue dynamics, tillageSystems and cover crop use for Indiana for last decade…

Overview of d productsOur system provides detailed maps of crop residue cover and cover crops:

Tillage in Fall & Spring Winter cover crops Annually Farm-field, county, &

watershed level Uncertainty maps Trends Continuous no-till and

cover cropping

Close to 90% of explainable variance

Example Products:Continuous no-till & cover crop intensity maps

• 2015 residue cover estimate in:

• Brown: 0-15%• Yellow: 15-50%• Green: > 50%

• RED indicates areas with at least five years of continuous no-till;

• PURPLE indicates areas with consistent winter cover cropping over the last 5 years;

OpTIS Next Steps: Goals:Support USDA Building Blocks Set baseline for conservation no-till/conservation tillage practices and cover

cropping for past decadeTimeline:Corn belt and Chesapeake Bay analyses by 2019National by 2021.

Thank you.

Questions?

Contact email: wsalas@agsemail.com