Carbon Sequestration and Nitrous Oxide Emission Mitigation Potential

Carbon Sequestration and Nitrous Oxide Emission Mitigation Potential of California's Croplands: A County Scale Assessment Using DNDC and GIS Databases. William Salas1, Marc Los Huertos2 and Changsheng Li3 1Applied

GeoSolutions, LLC, 10 Newmarket Road, Durham, NH, 03824 [email protected] 2Center for Agroecology and Sustainable Food Systems, University of California Santa Cruz 3 Complex Systems Research Center, University of New Hampshire, Durham, NH 03824

Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003

©Applied GeoSolutions, LLC

Outline • Overview of Model: DeNitrificationDeComposition or DNDC – Model structure and modeling approach – Validation – Scaling up from site to regional scale

• Ongoing Carbon Scoping Project: County scale assessment of carbon sequestration potential and trace gas emission from California croplands…

• Linking Science and Policy:Role of DSS Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Figure 2

The DNDC Model Ecological drivers

Climate

Vegetation

Soil

Anthropogenic activity

Plant growth Daily water demand

Daily potential ET

Annual average temperature

Daily biomass accumulation ( LAI )

Water uptake by roots

LAI-regulated albedo

Evaporation

Stalks

Soil temperature profile

Soil moisture profile

Oxygen diffusion

Soil Eh profile

Resistant litter

Labile microbes

Resistant microbes

Labile humads

Resistent humads

NH4

Daily N uptake by roots Root respiration

Labile litter

Grain

Water stress

Water flow between layers

Transpiration

Very labile litter

CO2

N demand

Roots

Oxygen consumption

DOC Passive humus

Soil climate

Effect of temperature and moisture on decomposition

Soil environmental variables

Temperature

NO2 -

Nitrate denitrifier

NO

Moisture

NO3

-

Nitrite denitrifier

N2O

pH

DOC

Decomposition

Substrates (NH4+, NO3- and DOC)

Eh

Nitrifiers

NH4+

NH3

NO3-

Clay-NH4+

Soil Eh

CH4 production

Aerenchyma

CH4 oxidation

CH4

DOC N2

Denitrification

CH4 transport

DOC

N2O denitrifier

N2O

NO

NH3

Nitrification


Fermentation


How DNDC Links Management to C and N Dynamics: Elemental cycling

Biochemical & geochemical reactions

Environmental factors

Ecological drivers

Mechanical movement Dissolution / crystallization

Gravity Radiation

Combination / decomposition Transport and transformation of chemical elements

Climate

Temperature

Soil

Moisture Oxidation / reduction Adsorption / desorption Complexation / decomplexation

Vegetation

Eh

Anthropogenic activity

pH Substrate concentration gradient

Assimilation / dissimilation Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


DNDC Links Ecological Drivers to Crop Yield/Trace Gas Emission

INPUT

PROCESSES

Climate - Temperature - Precipitation

Soil properties - Texture - Organic matter - Bulk density - pH

Management - Crop type - Tillage - Fertilization - Manure use - Irrigation - Grazing

OUTPUT

Used by microbes

Production of CO2, CH4, N2O, NO, N2, and NH3

DNDC containing fundamental biochemical & geochemical processes

Dynamics of soil water, NH4, NO3, and DOC

Competition

Used by plants


Growth of crop biomass


Input Parameters Required by DNDC 1. Climate:

- Daily max and min temperature; - Daily precipitation; - Atmospheric N deposition;

2. Soil:

- Bulk density; - Texture (clay fraction); - Total organic C content; - pH;

3. Management::

- Crop: type, cultivars, and rotation; - Tillage: timing and depth; - Fertilization: timing, type, amount, and depth; - Manure amendment: timing, type, and amount; - Irrigation: timing, amount, and pH; - Weeding: timing; - Grazing: livestock type, intensity, and season.



DNDC Predicts 1. 2. 3. 4.

Crop growth and yield; Soil organic C and N pools; N leaching; Gas emissions: CO2, CH4, N2O, NO, N2 and NH3



DNDC Validation • Approach: – Site scale validation – Independent researchers – Sites have covered wide range of agro-ecosystems, soils and climate conditions – California: need for field data…

• Regional Applications: scaling site to region – Uncertainty analyses based on variability of input parameters: Most Sensitive Factor (MSF), Monte Carlo, and Latin Hypercube Sampling (LHS)



Testing DNDC at Site Scale



Comparison on CO2 emissions from a silty loam soil in a tilled and fertilized winter wheat field in Columbia, Missouri 40 Tillage

Winter wheat field CO2 emission rate, kg C/ha/day

30

20

10

0 30

60

90

120

150

180

210

240

270

300

330

360

Julian day Measured CO2

Simulated total CO2

Simulated root respiration



150-Year Simulation for Soil C Dynamics in A Winter Wheat Field with Different Cropping Practices at Rothamsted Station, UK 90,000

80,000

Soil C, kg C/ha

70,000

Fallow year

Winter wheat field

60,000

50,000

40,000

30,000

20,000 1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

Year Simulated Simulated Simulated Field Field Field control fertilized manured control fertilized manured Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Validated SOC dynamics across range of crops and climate conditions…

Comparison of observed (points) and modeled (lines) long-term SOC dynamics at the agricultural sites in the U.S., the U.K, Canada, Germany, Australia, and China. Field datasets are from USDA 1992, Odell et al. 1984, Jenkinson 1991, Grant (unpublished), Li et al. 1997, Eduardo (unpublished), and Li et al. 2003. Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Observed and DNDC-Modeled N2O Fluxes from Agricultural Soils in the U.S., Canada, the U.K., Germany, New Zealand, China, Japan, and Costa Rica

Modeled N2O flux, kg N/ha/year

1000

100

0.4

0.34 0. 0.43

0.41 0.

0.4

0.37

10

0.032

0.035 0.037

0.011

0.015

0.035 0.029 0.033

0.035

1 0.1

1

0.01 0.015

0.01 0.02 0.025 0.006

0.019

0.032 0.032 0.05

0.032

0.031

0.05 0.029

0.

0.028

0.029

0.

10

100

0.025

0.019

0.1

Observed N2O flux, kg N/ha/year



1000

DNDC: Modeling CH4 emissions from rice paddy

CH4 emission

Ebullition

Plant-mediated transport

CH4 oxidation

Soil CH4

Eh

Aerenchyma development

CH4 production

CO2

DOC

soilOxygen moisture

Flooding duration

Rhizodeposition


Decomposition

Root respirartion


CH4 fluxes from a paddy rice (cultivar Mars) plot (Plot 1) at Beaumont, Texas, 1994 Field data from Ron Sass, Rice University 20

C H4 f lu x , k g C /h a /d a y

15 10 5 0 -5 -10 -15 Julian day Modeled CH4 product ion

Modeled CH4 oxidat ion

Modeled CH4 f lux by plant

Modeled CH4 f lux by ebulit ion


Modeled CH4 f lux

Flied CH4 f lux


Comparison of Measured and Modeled CH4 Emissions from 5 Sites In the U.S., China and Italy

-1

-1

simulated mean CH4-Emissionen [g C ha d ]

6

5

Texas (Plot 1)

4

Texas (Plot 2)

3 Vercelli Texas (Plot 3) 2

1 Wuxian 0 0

1

2

3

4

5 -1

6

-1

measured mean CH4-Emissionen [g C ha d ]



N2O and NO from Forests: Comparisons between observed and modeled fluxes from 28 forest stands in Europe and the U.S. (from dissertation of Florian Stange, Fraunhofer Institute for Atmospheric Environmental Studies, GarmischPatenkerchin, Germany, 2000)



DNDC GHG, C and NO3 Validation Country Canada

Validation N2O fluxes from cropland

The U.K.

N2O fluxes from cropland and pasture

Germany

NO and N2O from forests

Denmark Austria Italy

NO and N2O from forests NO and N2O from forests N2O from cropland

Australia

CO2 and N2O from cropland and grassland N2O fluxes from cropland and pasture

New Zealand

USA

NO3 leaching from cropland

USA

CO2 and N2O from cropland and grassland

China

CO2, CH4and N2O from cropland and grassland

Japan Thailand Costa Rica

CO2 and N2O from cropland CH4 from cropland N2O from cropland

Application National N2O inventory for agricultural lands Developed UK-DNDC for national N2O inventory for agricultural lands National NO and N2O inventory for cropland and forests

National N2O inventory for agricultural lands

Reference Smith et al. 2002; Grant et al. 2002 Brown et al. 2001

Butterbach-Bahl et al. 2001; Stange et al. 2000 Stange et al. 2000 Stange et al. 2000 Mulligan 2002 (unpublished) Wang et al. 1997

Developed NZ-DNDC for national N2O inventory for agricultural lands

National C sequestration and N2O inventory for cropland National C sequestration, CH4 and N2O inventory for cropland

Regional N2O inventory for agricultural land


Andrew 2002 (unpublished)

Farahbakhshazad et al. 2003 (unpublished) Li et al. 1996 and 2002a

Xiu et al. 1999; Li et al. 2002b; Cai et al. 2002 Cai et al. 2002 Cai et al. 2002 Plant 1998 and 2000


Regional Applications of DNDC • Estimating uncertainties is a critical Issue in scaling site to region – Uncertainty analyses based on variability of input parameters: Most Sensitive Factor (MSF), Monte Carlo, and Latin Hypercube Sampling (LHS) – Outputs provided in ranges. (e.g. 3.5 to 5.4 kg N2O/ha)



Scaling Up from Site to Regions Field & lab experiments

Statistical data collection

Remote sensing data acquisition

Model development: predicting biochemical & geochemical processes at site scale

GIS database construction: providing climate, soil, vegetation, and management data at regional scale

Soil fertility determined by soil organic matter storage

Crop yield

Modeling with DNDC

Remote sensing analysis: improving crop acreage data & providing phenology data


Emissions of CO2, CH4, N2O, NO, N2, and NH3

Leaching of nitrate


Sensitivity to Inputs: MSF 1200

C H 4 f lu x , k g C /h a /y r

1000

800

600

400

200

Cultivar

Draining times

Straw amandment

Soil texture

SOC


Temperature change

M in im u m

M a x im u m

M in im u m

M a x im u m

1 2

-2 -1

4%

2%

1%

C la y

S n a d y c la y lo a m

S a n d y lo a m Loam

1000 2000

0

1 2

0

C-4 8

V -7 7 G E-T

M a rs

0

Texture+SOC Extreme scenario


Uncertainty Analyses: Monte Carlo vs MSF Sensitivity Analysis: Fresno Cotton Monte Carlo Analysis: 1000 DNDC runs 300 MSF Range: 2272 to 2655 kg C/ha)

250 75% in MSF Range

Frequency

200 150 100 50 0 2100

2200

Demonstration Results Only:

2300

2400

2500

2600

2700

2800

Annual C Sequestration kg C/ha



Frequency of N 2O Emissions: 1997 Cotton in Fresno 250

Frequency: 1000 Monte Carlo samples

MSF Range: 3.2 to 3.8 (kg N/ha) 200

150

100

50

0 2.8

2.9

3

Demonstration Results Only:

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4

N2O Emission Rate (kg N/ha)



Frequency of change in SOC for all pasture fields in Modoc, CA, 1997 600

500

Frequency, 1/2000

400

300

200

100

0 1700

1800

1900

2000

2100

2200

2300

2400

dSOC, kg C/ha/yr

Demonstration Results Only: Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Frequency of N2O fluxes from all pasture fields in Modoc, CA, 1997 600

500

Frequency, 1/2000

400

300

200

100

0 0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

2.3

2.5

2.7

N2O flux, kg N/ha/yr

Demonstration Results Only: Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Estimating Carbon Sequestration Potential at County Scale: Why use GIS Process-based models like DNDC? • Spatial and temporal variability in – Climate (inter-annual variability) – Soils – Management Impacts

• Long-term impacts? Decade? Century? • C and N coupling: Effect on GWP!









Carbon Sequestration and Trace Gas Emissions 60

118000

116000

50 114000

40

110000 Intensive tillage Notill

108000

106000

N2O flux, kg N/ha/yr

SOC, kg C/ha

112000

30

20

104000

10 102000

100000

0 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

2

3

4

Year

5

6

7

8

9

10

11

12

13

14

15

16

17

Year

Figure 1: DNDC modeled changes in soil organic carbon content (SOC) and nitrous oxide (N2O) emissions from a corn-soybean rotation under two different management systems. Initial soil conditions were set to be identical with the same nominal climate conditions. It is clear that a constant or even site specific emission factor for N2O would not capture the temporal dynamic of emissions.



18

19

20

Evaluating Management Alternatives for Mitigation of GHG emissions GWPi : Σ (CO2i + N2Oi * 310 + CH4i * 21; where GWPi (kg CO2 equivalent/ha/yr) is the Global Warming Potential induced by scenario i; CO2i, N2Oi and CH4i are CO2 flux (kg C/ha/yr), N2O flux (kg N/ha/yr) and CH4 flux (kg C/ha/yr), respectively, induced by scenario i. Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


DNDC-modeled C sequestration, N2O emissions and their global warming potentials (GWP) for a corn-soybean rotation system with different tillage approaches in Adair County, Iowa from 1994-2014 C sequestration kg C/ha/yr

N2O flux

SOC-GWP

kg N/ha/yr

N2O-GWP

Net GWP

kg CO2 equivalent/ha/yr

Intensive tillage

125

11.5

-459

5615

5156

Notill

468

21.1

-1716

10301

8585

Critical need for models to assess long-term impacts of management decisions!



he

at Al -ric fa e lfa Fa ric llo e w C -ri on ce ve n C on tion se a rv l til at l io age n til la ge N C o on til tin l uo 1 u s m f id se loo 2 di a m n s id se on g d 3 m aso rai n id se n d as rai o n ns 20 N dr o 00 ai st ns r k 40 g s aw 00 tra am en kg wst C a dm ra w me ent -C nd am me en nt 0 dm r 50 esid en % ue t re 90 si inco % du rp re e in or a si c du or ted po e in co rate rp d or at ed Am m on iu m Am bic Ure m a rb a on o iu nat m e su lfa t N e itr at e

W

GWP, kg CO2 equivalent/ha/yr

Summary of Management Alternatives and GWP 90000

80000

70000

60000

50000

40000

30000

20000

10000

0

Alternative management

Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003 ©Applied GeoSolutions, LLC

Ongoing Carbon Scoping Project: :

¾Objective: County scale assessment of carbon sequestration and trace gas emission from California croplands ¾Data… ¾Demonstrate model capabilities ¾Next steps…



Input Data • • • • •

Climate Data Soils Crop Areas Management Practices Scenarios for Carbon Sequestration



Climate Data Inputs: • Minimum Needs: Daily Tmax, Tmin, Precip • Sources: Station data: NCDC and CIMIS, gridded DAYMET (NCAR/U Mont) data • DAYMET: – Produces daily temp, precipitation, humidity and radiation based on station data – Performs interpolation based on “spatial convolution of a truncated Gaussian filter”

• Initial Analysis: Using 1997 and 1983 daily DAYMET data for station nearest County centroid. Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Temperature (deg C)

Daily Min/Max Temperature: Fresno County (DAYMET Data: 1997 and 1983) 45 40 35 30 25 20 15 10 5 0 -5 1

31

61

91

121

151

181

211

241

271

301

331

361

DOY 1997 Avg 1997: max 25.9 °C min 11.2 °C

1997 maxT (°C)

1997 minT (°C)

1983 maxT (°C)

1983 minT (°C)


Avg 1983: max 24.2 °C min 10.5 °C


Daily Precipitation: Fresno County (DAYMET 1997 and 1983)

Daily Precipitation (cm)

3.5 3 2.5 2 1.5 1 0.5 0 0

25

50

75

100 125 150 175 200 225 250 275 300 325 350 DOY

1997 Total: 27.5cm 1983 Total: 64.8cm

1997 Precip (cm)

1983 Precip (cm)



Soils • NRCS STATSGO Soils data ¾DWR crop area mask ¾Derived area weighted statistics of range (min, max) in SOC, pH, Texture (%clay), and bulk density by county





5.0%

4.0%

3.0%

0.0%

0.0%

Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003 SAN BENITO SAN SAN DIEGO SAN JOAQUIN SAN LUIS OBISPO SANTA SANTA CRUZ SHASTA SOLANO STANISLAUS SUTTER TEHAMA TRINITY TULARE TUOLUMNE YOLO YUBA

1.0% Content

2.0%

10.0%

AMADOR BUTTE COLUSA CONTRA COSTA DEL NORTE FRESNO GLENN HUMBOLDT IMPERIAL KERN KINGS LAKE LASSEN Clay LOS ANGELES MADERA MARIN MARIPOSA MERCED MODOC MONTEREY AMADOR PLACER BUTTE PLUMAS COLUSA RIVERSIDE CONTRA COSTA SACRAMENTO DEL NORTE SAN BENITO FRESNO SAN GLENN SAN DIEGO HUMBOLDT IMPERIAL SAN JOAQUIN KERN SAN LUIS OBISPO KINGS SANTA LAKE SANTA CRUZ LASSEN SHASTA LOS ANGELES SOLANO MADERA STANISLAUS MARIN SUTTER MARIPOSA TEHAMA MERCED MODOC TRINITY MONTEREY TULARE PLACER TUOLUMNE PLUMAS YOLO RIVERSIDE YUBA SACRAMENTO

SOC

County Soils: SOC

(Derived from NRCS STATSGO)

6.0%

County Soils: Clay Content (Derived from NRCS STATSGO)

60.0%

50.0%

40.0%

30.0%

20.0%


Bulk Density

7.5

6.5

5.5

1.0 AMADOR BUTTE COLUSA CONTRA COSTA DEL NORTE FRESNO GLENN HUMBOLDT IMPERIAL KERN KINGS LAKE LASSEN LOS ANGELES MADERA MARIN MARIPOSA MERCED MODOC MONTEREY PLACER PLUMAS RIVERSIDE SACRAMENTO SAN BENITO SAN SAN DIEGO SAN JOAQUIN SAN LUIS OBISPO SANTA SANTA CRUZ SHASTA SOLANO STANISLAUS SUTTER TEHAMA TRINITY TULARE TUOLUMNE YOLO YUBA

4.5 AMADOR BUTTE COLUSA CONTRA COSTA DEL NORTE FRESNO GLENN HUMBOLDT IMPERIAL KERN KINGS LAKE LASSEN LOS ANGELES MADERA MARIN MARIPOSA MERCED MODOC MONTEREY PLACER PLUMAS RIVERSIDE SACRAMENTO SAN BENITO SAN SAN DIEGO SAN JOAQUIN SAN LUIS OBISPO SANTA SANTA CRUZ SHASTA SOLANO STANISLAUS SUTTER TEHAMA TRINITY TULARE TUOLUMNE YOLO YUBA

Soil pH

County Soils: pH

(Derived from NRCS STATSGO)

9.5

8.5

County Soils: Bulk Density (Derived from NRCS STATSGO)

1.7

1.6

1.5

1.4

1.3

1.2

1.1

Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003 ©Applied GeoSolutions, LLC

County Agricultural Data • Various Sources of California data: County Commissioners Reports, FRAP (Fire Resource & Assessment Program, CDF), NASS, DWR Æ All have pluses and minuses! • Used DWR mid-1990s data: – Sub-county spatial resolution – Based on Aerial Photos coupled with field surveys – Total crop area: 38,344km2 Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


county fips cotton sugarbeet corn sorghum beans,dry sunflower other field barley wheat oats other grain alfalfa other pastuci AMADOR 6005 0 23 196 0 0 0 191 81 81 81 81 117 914 BUTTE 6007 0 1139 917 0 1056 359 1374 0 5011 0 124 1321 3439 COLUSA 6011 2564 470 1940 234 2748 606 6534 84 11580 0 1989 3185 1633 CONTRA COS 6013 0 0 3995 0 164 0 1671 715 715 715 715 1622 2865 DEL NORTE 6015 0 0 0 0 0 0 22 0 0 0 0 0 3806 FRESNO 6019 178197 7272 9913 0 726 5 11147 8706 8706 8706 8706 35386 7172 GLENN 6021 333 376 7880 227 1653 2933 1682 489 6670 529 2881 5601 9959 HUMBOLDT 6023 0 0 106 0 37 0 154 0 0 0 957 192 16914 IMPERIAL 6025 123 27535 1988 0 50 0 653 1418 40825 152 1728 75197 18479 KERN 6029 97782 2300 13834 403 3510 0 9150 47908 0 0 0 43473 3730 KINGS 6031 108314 140 24753 1391 5 0 31972 7237 7237 7237 7237 17637 1619 LAKE 6033 0 0 0 0 0 0 291 6 25 300 499 149 2404 6037 0 0 0 0 0 0 0 0 0 0 0 0 0 LOS ANGELES LASSEN 6035 0 63 10 3 1296 0 0 24 1785 2253 1514 15252 27467 MADERA 6039 20393 159 6173 15 3515 0 733 11298 0 0 0 14637 4688 MARIN 6041 0 0 0 0 0 0 0 0 0 0 1912 0 645 MARIPOSA 6043 0 0 0 0 0 0 0 1 1 1 1 0 1327 MERCED 6047 37110 3486 24124 0 68 0 2365 3792 3792 3792 3792 32950 24151 MODOC 6049 0 1691 0 0 1895 0 201 8274 1619 376 2067 15480 39038 MONTEREY 6053 0 35 439 0 0 0 2154 3795 3795 3795 3795 937 1169 PLACER 6061 0 0 241 0 0 0 191 0 0 0 2467 6 7516 PLUMAS 6063 0 0 0 0 0 0 0 268 0 0 109 2586 13802 RIVERSIDE 6065 0 0 343 0 323 0 475 3879 10962 2146 1427 3511 3054 6067 0 1190 17179 390 0 0 5030 160 2651 0 5163 2988 12491 SACRAMENTO SAN BENITO 6069 0 85 1065 0 0 0 656 2355 2355 2355 2355 531 439 6071 0 0 549 0 0 0 481 114 0 1082 64 1076 1637 SAN BERNARD SAN DIEGO 6073 0 0 66 0 6868 648 4 38 1421 2107 0 29 2748 6077 0 2729 30212 247 20 0 8245 0 0 0 30445 24850 14200 SAN JOAQUIN 6079 0 0 53 0 1697 0 291 22788 2847 10186 16778 1711 2888 SAN LUIS OBIS SANTA BARBA 6083 0 0 413 0 0 0 34 0 2827 2928 0 1037 2565 SANTA CRUZ 6087 0 0 8 0 0 0 38 34 34 34 34 4 454 SHASTA 6089 0 227 4 5 2042 1633 171 258 23 185 874 2366 17016 SOLANO 6095 0 4107 8006 20 12675 1 3384 0 0 0 27628 9233 7586 STANISLAUS 6099 28 27 23957 0 3977 263 694 1406 1406 1406 1406 13989 25657 SUTTER 6101 268 1528 2830 99 173 88 10068 0 0 2863 5061 2040 2636 TEHAMA 6103 0 157 1027 0 5101 15 1107 247 2800 439 2848 2147 13006 TRINITY 6105 0 0 0 0 0 0 4 0 0 69 0 16 946 TULARE 6107 31613 1821 45749 1167 786 2855 2481 28769 0 0 0 39562 3311 TUOLUMNE 6109 0 0 0 0 0 0 0 0 0 0 0 230 230 YOLO 6113 2051 2052 17798 617 39 0 14367 0 0 0 30002 16865 0 YUBA 6115 0 0 379 0 0 0 246 0 0 154 399 5802 0



GIS Database



DWR Crop Areas (km^2) Total Crop Area: 38,344 km^2 6000 5000 4000 3000 2000 1000

ck

t ru

f ru

it

&

nu

tT

re

e co s gr - l tt on ap et es tu ce -w i n al f a e & lf a ta pa bl e st ur e m c or isc n gr r ai n ic e (o at s ba ) rle m y isc c it ru f ie ld w s - s he or a gh t um be et oa t be s s u an nf s lo s o we rg r hu m

0



Management Data COTTON Farming Operations Land Preparation Land Preparation Seed Bed Preparation

Crop Cycles Per Year(1)

Passes Per Crop Cycle(2)

Fraction Acreage Per Cycle(3)

1 1

4 2

1.0 1.0

1

1

1.0

1

3

1.0

1

1

1.0

1

1

1.0

Passes During Month Jan Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Planting

Growing Season Operation

Harvesting

Postharvest Shredding

(1) Crop cycles per year refers to the number of times per year a particular farming operation is performed. A value less than one indicates an operation is performed less than once per year. Values greater than one indicate the operation is done more than once per year.

Land Prep Acre-Passes Land prep 1x4x1=4 Seed bed prep 1 x 2 x 1 = 2 ----------------Total acre passes 6

(2) Passes per crop cycle refers to the actual number of passes by a farm implement Necessary to accomplish a particular farming operation. (3) Fraction acreage per cycle refers to the fraction of the acreage covered by the particular farming operation. For example, in an orchard or a vineyard, operations usually only disturb the ground between the rows. In those cases only 50% of the acreage is actually affected by the operation. In contrast, a discing operation usually affects 100% of the acreage.

Source: CARB Fugitive Dust Study Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Management Data FIELD CORN Farming Operations Land Preparation Stubble Disc Finish Disc List & Fertilize Mulch Beds

Crop Cycles Per Year(1)

Passes Per Crop Cycle(2)

Fraction Acreage Per Cycle(3)

Passes During Month Jan Feb

1 1 1 1

1 1 1 1

1.0 1.0 1.0 1.0

1

1

1.0

1

2

1.0

1

1

1.0

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Planting

Cultivation

Harvesting

Postharvest

(1) Crop cycles per year refers to the number of times per year a particular farming operation is performed. A value less than one indicates an operation is performed less than once per year. Values greater than one indicate the operation is done more than once per year.

Land Prep. Acre-Passes Stubble Disc 1 x 1 x 1 = 1 Finish Disc 1 x 1 x 1 = 1 List 1x1x1=1 Mulch 1x1x1=1 ----------------Total acre passes 4

(2) Passes per crop cycle refers to the actual number of passes by a farm implement Necessary to accomplish a particular farming operation. (3) Fraction acreage per cycle refers to the fraction of the acreage covered by the particular farming operation. For example, in an orchard or a vineyard, operations usually only disturb the ground between the rows. In those cases only 50% of the acreage is actually affected by the operation. In contrast, a discing operation usually affects 100% of the acreage.

Source: CARB Fugitive Dust Study Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Fertilizer Application Rates DWR Crop Class C D D12 F G I P P1 R T V

Description

Fertilizer Application Rates (kg N/ha)

Citrus & Subtropical Fruit Decid Fruit & Nuts (non-almond) Almonds Field Crops Grain and Hay Idle Pasture Grass (non N-fixing) Alfalfa Pasture Rice Truck Crops (vegetables) Vineyard

SJV 150 140 100 140 100 0 100 0 100 250 70

Sac 140 110 80 140 100 0 100 0 100 210 60

CCst 140 110 80 140 100 0 100 0 100 300 60

Imp 125 140 100 140 100 0 100 0 100 300 75

Initial Baseline Analysis did not differentiate across regions Source: Potter et al 2001. (analysis of NH3 emissions for CARB) Presented at Soil Carbon Sequestration Workshop, Kearney Foundation Soil Science, September 2003


Next Steps • Fertilizer: use different application rates across regions DWR Crop Class C D D12 F G I P P1 R T V

Description

Citrus & Subtropical Fruit Decid Fruit & Nuts (non-almond) Almonds Field Crops Grain and Hay Idle Pasture Grass (non N-fixing) Alfalfa Pasture Rice Truck Crops (vegetables) Vineyard

Fertilizer Application Rates (kg N/ha) SJV 150 140 100 140 100 0 100 0 100 250 70

Sac 140 110 80 140 100 0 100 0 100 210 60


CCst 140 110 80 140 100 0 100 0 100 300 60

Imp 125 140 100 140 100 0 100 0 100 300 75


Next Steps cont. • Soils: Use crop class specific soils data at the county scale. Merge DWR and STATSGO • Merced Soil Characteristics: CROP G R F P T D/C/V I

AREA(m^2) 154433722.4 26424198.3 721508949.3 574194178.3 163349689.3 537401774.7 NA

CLAY FRACTION A.W.Min A.W.Max 17.63561 25.79246 19.61089 28.58731 21.73627 28.47172 18.52837 25.73746 19.36045 25.1621 8.545795 14.71284 NA NA

BULK DENSITY A.W.Min A.W.Max 1.394909 1.495448 1.343949 1.443949 1.400843 1.503235 1.415494 1.517749 1.43223 1.532635 1.513845 1.61709 NA NA

ORGANIC MATTER A.W.Min A.W.Max 0.671436 1.804702 0.603981 1.252003 0.797725 2.348781 0.734353 2.109567 0.755769 1.796073 0.625974 1.565343 NA NA


pH A.W.Min 6.966689 7.760544 7.069387 6.981879 6.771935 6.189352 NA

A.W.Max 8.463362 9.318588 8.475595 8.473096 8.097954 7.754332 NA


Next Steps cont. • Validation analyses for California. Critical! Need to collect existing data. Source? – Long-term SOC changes. – N2O data – CH4 from rice

• Evaluate scenarios for C sequestration: cover crops, conservation tillage, notill, climate change, … • Run 20 and 40 year scenarios to examine C sequestration capacity and net GWP (N2O offsets)



Linking Science to Policy: Role for Decision Support Systems GIS Data Server: Climate: NCDC & DAYMET Soils: NRCS STATSGO & SUSRGO

DNDC Biogeochemical Processor:

Ag Census NASS. DWR Land use, etc

Long-term soil fertility: SOC Emissions of CO2, CH4, N2O, N2, and NH3

Field Studies: Model refinement Validation Auditing/verification

Remote Sensing Product Generation Data: IKONOS, Landsat ETM, MODIS RS Products: Crop Type, Crop Phenology, including planting and harvesting dates and LAI, Management Data, including • Tillage practice (conventional, vs no-till) • Tillage timing • Use of cover crops • Irrigation • Residue management

Nitrate Leaching

Management Parameter Server: -system will contain defaults for all management parameters (e.g. fertilizer, manure, tillage, crop cycles, crop types, irrigation, etc. ) -users can then change management parameters and modify soil properties on a field by field basis if they have access to better soils data. Individual fields can be selected using GIS tools and aerial photos from aerial photo server (see figure for example).

Record Keeping System -tracks historical management -keeps inventories of GHG, SOC, NH3 and nitrate fluxes.

GIS Map Server - creates user defined maps. Sensitivity and Error Tracking System -

provides estimates sensitivity estimates tracks differences between field and Model estimates


Report Generator -produces summary reports with maps - incorporates regulatory guidelines regarding fert and manure applications


Thank You! Acknowledgments: Ongoing California Countyscale analysis is supported by joint Kearney Foundation California Energy Commission and California Department of Food and Agriculture project.

