AARHUS
UNIVERSITY
NH3 Emissions from Fertilisers
Nick Hutchings, Aarhus University
J Webb, Ricardo-AEA
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AARHUS
UNIVERSITY
Some history
• Lack of scientific documentation for the Guidebook methodology
• Review of literature (AU Environmental Sciences)
• Mean emission factor for each fertiliser type
• Some increases in emission factors (especially urea)
• Additional data found (Bouwman et al 2002 database)
• More detailed analysis (AU Agroecology + Ricardo-AEA)
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AARHUS
UNIVERSITY
Statistical analysis (1)
• Variables considered:
• Fertiliser type
• Measurement method
• Location (indoor, outdoor)
• Application method (broadcast, incorporated etc)
• Soil type
• Soil pH
• Soil CEC
• Crop type (bare soil, grass, maize, rice, other cereals)
• Temperature
• Rainfall intensity (mm/day)
• Data are unbalanced
• Many data are missing from individual observations
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AARHUS
UNIVERSITY
Statistical analysis (2)
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No significant differences between measurement methods or crop types
Significant differences between indoor/outdoor and application method
For Guidebook, estimate emissions for application outdoor and broadcast
Group fertilisers into types:
• Urea
• Fertilisers containing urea (e.g. UAN, UAS)
• Fertilisers not containing urea (e.g. CAN, AN, AS)
• Assume effect of soil and climate characteristics operate independently:
• Soil characteristics – soil pH, soil CEC. Assume fertiliser type x pH interaction
• Climate characteristics – temperature and rainfall intensity
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AARHUS
UNIVERSITY
Results
• Significant differences between urea (U), fertilisers with urea (U+) and fertilisers
without urea (U-)
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AARHUS
UNIVERSITY
Results
• Significant differences between urea (U), fertilisers with urea (U+) and fertilisers
without urea (U-)
• Significant positive effect of soil pH
• Significant fertiliser type x soil pH interaction
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AARHUS
UNIVERSITY
Results
• Significant differences between urea (U), fertilisers with urea (U+) and fertilisers
without urea (U-)
• Significant postive effect of soil pH
• Significant fertiliser type x soil pH interaction
• Significant negative effect of soil CEC
• Only for U
ORIGINAL
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UNIVERSITY
Results
REVISED
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AARHUS
UNIVERSITY
Results
• Significant differences between urea (U), fertilisers with urea (U+) and fertilisers
without urea (U-)
• Significant postive effect of soil pH
• Significant fertiliser type x soil pH interaction
• Significant negative effect of soil CEC
• Only for U
• Significant effect of temperature
• Correlation between temperature and soil moisture?
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AARHUS
UNIVERSITY
Results
• Significant differences between urea (U), fertilisers with urea (U+) and fertilisers
without urea (U-)
• Significant postive effect of soil pH
• Significant fertiliser type x soil pH interaction
• Significant negative effect of soil CEC
• Only for U
• Significant effect of temperature
• Correlation between temperature and soil moisture?
• Strong negative effect of rainfall intensity
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AARHUS
UNIVERSITY
Examples
• pH 7, soil CEC 20 meq/100g
• Strasbourg:
• March: 6º C, rainfall intensity 1.2 mm/day = 12% (U), 12% (U+), 3% (U-)
• June: 17º C, rainfall intensity 2.5 mm/day = 13% (U), 13% (U+), 4% (U-)
• Florence:
• February: 7º C, rainfall intensity 2.5 mm/day = 6% (U), 7% (U+), 2% (U-)
• June: 22º C, rainfall intensity 1.8 mm/day = 18% (U), 19% (U+), 6% (U-)
• Current emission factors:
• pH ≤ 7 U 21%, U+ 11 to 16%, U- 1 to 9%
• pH >7 U 21%, U+ 11 to 16%, U- 1 to 25%
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UNIVERSITY
Development of Tiered methodologies
• Develop Tier 3
• Use Tier 3 to develop Tier 2
• Use Tier 2 to develop Tier 1
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AARHUS
UNIVERSITY
Tier 3 methodology
• Use a model that accounts for:
• Fertiliser type (U, U+, U-)
• Soil pH and soil CEC
• Temperature and rainfall intensity
• Need to know how much of each type of fertiliser used on which soil types (pH
and CEC) and when (temperature and rainfall intensity):
• Parties wishing to use this Tier 3 need these data
• For Tier 2, make assumptions
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UNIVERSITY
Tier 2 methodology
• Use agro-ecological zones
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AARHUS
UNIVERSITY
Tier 2 methodology in practice
• Divide land area between agro-ecological zones (AEZ)
• Partition each AEZ into areas with soil pH >7 and pH ≤ 7
• For Europe, use European Soil Database
• Partition each AEZ x soil pH area between crop types (“grass + double cropping”
or “all other crops”)
• For Europe, JRC resources?
• For each fertiliser type, partition the national amount used between the different
AEZ x soil pH x crop combinations in proportion to their contribution to the total
land area.
• Use the emission factor for each AEZ x soil pH x crop combination to calculate
the ammonia emission
• Sum the ammonia emissions from each AEZ x soil pH x crop combination to
calculate the total ammonia emission
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UNIVERSITY
Land area
AEZ 2
AEZ 1
pH>7
Grass
Other
crops
pH≤ 7
Grass
Other
crops
pH>7
Grass
pH≤ 7
Other
Grass
crops
Other
crops
Partition each fertiliser type to these areas
Multiply by the emission factor specified for
each area
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UNIVERSITY
Tier 2 methodology emission factors (1)
• Obtain mean daily air temperature and monthly rainfall for 5-6 locations within
each AEZ
• Calculate mean rainfall intensity per location
• Estimate start of the growing season
• For grass, start is monthly air temperature >=6ºC, for other crops, >=8ºC
• Estimate end of the growing season for grass
• Monthly air temperature <6ºC
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AARHUS
UNIVERSITY
Tier 2 methodology emission factors (2)
• For grass, assume fertiliser is applied at the start of every full 6 week of the
growing season
• Calculate application dates
• Calculate air temperature and rainfall intensity at these dates
• Use Tier 3 model to calculate emission factors for each date
• Calculate the average emission factor
• For other crops, assume fertiliser is applied at the start of the growing season and
then 6 weeks later
• Repeat procedure as for grass
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UNIVERSITY
Tier 1 methodology
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Fertiliser consumption by type is available for all countries (FAO)
Assume that 50% of the land has a soil pH ≤ 7, 50% >7
Assume that the grass:other crop area is 50:50
Calculate an emission factor specific for the U, U+, U- types
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AARHUS
UNIVERSITY
Conclusions
• Data in scientific literature allows a Tier 3 methodology to be developed
• Dataset is unbalanced (not all important variables measured in all experiments)
• Data from commonly-used low-emission fertilisers (e.g. CAN) are under-represented
• Data from commonly-used high-emission fertilisers (e.g. urea) are over-represented
• Aggregation of data from different fertiliser types was necessary
• Additional, standardised and balanced measurement experiments are required
• Focus on commonly-used fertiliser types
• Focus on most important variables
• Further work required to complete development of Tier methodologies
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