AARHUS UNIVERSITY NH 3 Emissions from Fertilisers Nick Hutchings, Aarhus University J Webb, Ricardo-AEA 1

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) 2

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 3

AARHUS UNIVERSITY Statistical analysis (2) 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 4

AARHUS UNIVERSITY Results Significant differences between urea (U), fertilisers with urea (U+) and fertilisers without urea (U-) 5

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 6

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 7 ORIGINAL

AARHUS UNIVERSITY Results 8 REVISED

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? 9

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 10

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% 11

AARHUS UNIVERSITY Development of Tiered methodologies Develop Tier 3 Use Tier 3 to develop Tier 2 Use Tier 2 to develop Tier 1 12

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 13

AARHUS UNIVERSITY Tier 2 methodology Use agro-ecological zones 14

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 15

AARHUS UNIVERSITY 16 Land area AEZ 1 AEZ 2 pH>7 pH≤ 7 pH>7 pH≤ 7 Grass Other crops Grass Other crops Grass Other crops Grass Other crops Partition each fertiliser type to these areas Multiply by the emission factor specified for each area

AARHUS 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 17

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 18

AARHUS UNIVERSITY Tier 1 methodology 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 19

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 20