2010.Estimation of N2O Emission Factors for Soils Depending on Environmental Conditions and Crop...
-
Upload
huong-luong -
Category
Documents
-
view
27 -
download
0
Transcript of 2010.Estimation of N2O Emission Factors for Soils Depending on Environmental Conditions and Crop...
Estimation of N2O emission factors for soils depending on environmental conditions and crop managementJ.P. LesschenG.L. VelthofJ. KrosW. de Vries
Outline
Introduction Conceptual framework Factors controlling N2O emissions Results Validation based on Stehfest and Bouwman data set Conclusions
Introduction
N2O contributes 7.9% to the global GHG emissions Agriculture is the main source of N2O and soil
emissions account for most of the emissions Soil N2O emissions often estimated with default
IPCC emission factor of 1% of applied N Large variation exists depending on environmental
and management factors
Introduction
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0 50 100 150 200 250 300 350
Sample ID
N2O
em
issi
on fa
ctor
Variability N2O emission factors Stehfest and Bouwman data set
Objective
To develop a simple N2O emission factor inference scheme, based on environmental and management factors
Validate the approach with the Stehfest and Bouwman data set
Conceptual framework
Emission factor approach differentiated to manure type, soil type, land use, climate, etc.
Define reference situation with emission factor of 1% Define changes in emission factor caused by factors
Factor Denitrification N2O/N2 ratioIncreasing nitrate content + +Increasing oxygen content - +Increasing available organic carbon + -Increasing temperature + - Decreasing pH - +
Reference situation
Starting point is EF for fertilizer of 1% of applied N Two-year monitoring study in Netherlands (Velthof et
al., 1996) with the following conditions: Grassland Well-drained sandy soil Fertilized with calcium ammonium nitrate fertilizer Neutral pH (> 5) Average precipitation (600-900 mm/ year)
Nitrogen input
Sources of nitrogen: Mineral fertilizer: NO3 fertilizer, NH4 fertilizer and urea Manure:
cattle, pig and poultry Manure type: solid or slurry Application technique: surface or injection
Grazing Biological N fixation Crop residues: cereals, vegetables and other crops Atmospheric N deposition Net mineralization of soil organic N
Example: effect of fertilizer type
(Pathak and Nedwell, 2001)
grassland
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Urea Ammonium sulphate Potassium nitrate Ammonium nitrate
Field capacitySubmerged
N2O emission factor, % of N applied
Example: manure and application type
(Velthof and Mosquera, 2010)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Cattle manureshallowinjection
Cattle manurebroadcast
Pig manureinjection
Pig manurebroadcast
N2O
em
issi
on fa
ctor
Grassland
Maize land
Example: crop residues - nitrate
0
2
4
6
8
10
12
14
16
Wheat Maize Barley Cabbage Sprouts Mustard Broccoli Sugarbeet
N2O-emission, % of crop residue N
(Velthof et al., NCA 2002)
Calculation rules EF factors for N input
Mineral fertilizer Grassland: NO3 : NH4 : urea = 2 : 1 : 1 Arable land: NO3 : NH4 : urea = 1.25 : 1 : 1
Manure poultry manure : solid cattle manure : solid pig manure : cattle slurry :
pig slurry = 1 : 1 : 1 : 2 : 3 EF for injected or incorporated manure is 1.5 times EF of surface-
applied manure Grazing: EF is 2 times EF of NO3 fertilizer Crop residues: cereals : vegetables : other crops = 0.2% : 2%
: 1% (for reference situation)
Oxygen content
Indirect parameters for the effects of oxygen content: Soil type: texture, organic matter and groundwater
level Precipitation: precipitation increases risk on
anaerobic conditions Land use: in grasslands more organic C and higher
oxygen consumption Manure application technique: the depth of
application affects oxygen content
Example: effect of soil type
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
sand clay
no fertilizer
NH4NO3
cattle slurry
N2O emission, kg N per ha
(Van Groenigen et al., Plant & Soil. 2004)
Maize
Precipitation
Precipitation is an indicator for the risk of anaerobic conditions in soils
Several studies find significant relations between precipitation and N2O EF
Linear regression based on Stehfest and Bouwman data set (aggregated to location, n=45)
500 : 750 : 1000 mm = 0.37 : 1 : 1.63
y = 3E-05x - 0.012R2 = 0.16
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0 500 1000 1500
Precipitation (mm)
N2O
em
issi
on fa
ctor
Available organic Carbon content
Indirect parameters for the effects of available carbon content:
Soil type: peat soils much higher than clay and sand Sand : Clay : Peat = 1 : 1.5 : 2
Land use: grassland higher EF than arable land for mineral fertilizer but lower for manure
Three manure types Three crop residue types
Temperature
Temperature affects activity of nitrifying and denitrifying bacteria and the ratio N2O/ N2
Lower EF with lower temperature
No significant relation found based on Stehfest and Bouwman data set
Temperature influence is mainly seasonal related
Not included0.00
0.02
0.04
0.06
0.08
0.10
4 6 8 10 12 14 16 18Annual temperature (degrees C)
N 2O
em
issi
on fa
ctor
pH
pH affects the activity of nitrifying and denitrifying bacteria with optimum activities at pH 7-8
Calculation rule: in acid soils (pH < 5) N2O emission factor is 25% lower than in other soils
n = 22
n = 330
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
pH < 5 pH > 5
N 2O
em
issi
on fa
ctor
Emission factor inference schemeSoil type Land use pH Emission factor in % of the N input
nitrate containing fertilizer
ammonium fertilizer
urea pig slurry low NH3
application
cattle slurry low NH3
application
poultry manure; low NH3
application
Sand grassland < 5 0.75 0.38 0.38 0.56 0.38 0.19> 5 1.00 0.50 0.50 0.75 0.50 0.25
Sand arable land < 5 0.38 0.30 0.30 0.84 0.56 0.28> 5 0.50 0.40 0.40 1.13 0.75 0.38
Clay grassland < 5 1.13 0.56 0.56 0.84 0.56 0.28> 5 1.50 0.75 0.75 1.13 0.75 0.38
Clay arable land < 5 0.56 0.45 0.45 1.27 0.84 0.42> 5 0.75 0.60 0.60 1.69 1.13 0.56
Peat grassland < 5 1.50 0.75 0.75 1.13 0.75 0.38> 5 2.00 1.00 1.00 1.50 1.00 0.50
Peat arable land < 5 0.75 0.60 0.60 1.69 1.13 0.56> 5 1.00 0.80 0.80 2.25 1.50 0.75
Results N2O soil emissions for EuropeINTEGRATOR: 292 kton N2O-N IPCC 1% EF: 315 kton N2O-N
Validation of the approach
Stehfest and Bouwman (2006) data set Selection agriculture in temperate zones (n = 1137) 352 cases with corrected N2O EF
Reclassification of dataset according to the factors in the inference framework
Addition of annual precipitation for missing cases in Europe Calculation of N2O emission factor for each case Comparison observed EF with:
Simulated EF (n = 225) IPCC 1% EF Empirical relation of Stehfest and Bouwman (2006):
log N2Oemission = sum Ei + A (n = 133)
Validation of N2O emission factors
Approach Average difference
RMSE Pearson correlation
n = 225 Simulation 0.88 1.59 0.440 **
IPCC 1% 1.06 1.75 -
n = 133 Simulation 0.76 1.46 0.243 **
IPCC 1% 0.87 1.49 -
Stehfest and Bouwman
0.91 1.59 0.093
Validation of N2O emission factors
0
10
20
30
40
50
60
0.25 0.75 1.25 1.75 2.25 2.75 3.25 More
N2O emission factor (%)
Freq
uenc
yEF observed
EF simulated
EF Stehfest and Bouwman (2006)
Conclusions
The presented approach takes account of environmental and management factors
The proposed approach performs better than the IPCC EF and the Stehfest and Bouwman relation
Benefits: Mitigation measures can be better accounted for Regional variation is better expressed The EF inference scheme offers possibility to use a Tier2
approach for reporting N2O emissions
Thank You!
© Wageningen UR