Improving national GHG inventories for manure management: database - - PowerPoint PPT Presentation

Improving national GHG inventories for manure management: database

development & initial analysis

Tony van der Weerden,

Marta Alfaro, Barbara Amon, Ignacio Beltran, Cecile de Klein, Maguy Eugene, Peter Grace, Karin Groenestein, Sasha Hafner, Melynda Hassouna, Nicholas Hutchings, Dominika Krol, Alasdair Noble, Francisco Salazar, Rachel Thorman.

GHG emissions along the manure management chain

• Systems approach
• From Animal diet

(CEDERS) to Land application, including animal grazing

• Example shown for liquid

manure management

Modified from Sajeev et al. 2017

Animal diets

• Low protein

diets

Animal housing

• Manure

removal

Manure treatment

• Anaerobic

digestors

• Acidification

Manure covers

• Covers

Manure application

• Shallow

injection

Animal Grazing

• Soil conditions

Focus of DataMan

Housing Storage Field

CEDERS

GHG emissions from manure management

Transformation of

• rganic material ->

TAN, CH4, CO2 etc.

Methane (CH4)

• anaerobic lagoons, liquid and slurry storage

Nitrous oxide (N2O)

• solids and slurry storage (semi-anaerobic), deep bedding, land

application, excreta from grazing. Ammonia (NH3) – indirect source of N2O

• livestock housing, manure storage, land application, excreta from

grazing

DataMan: Expected outputs & benefits

1. Publicly accessible GHG emissions database – available for future expansion. 2. Functional relationships between key variables and EFs identified. 3. Mitigation options identified. 4. Disseminate findings & case studies to industry & governments on mitigation options and how to improve inventories.

Approach

• Identify key variables affecting N2O, NH3 and CH4 emissions from manure management:
• housing
• storage
• land-application,
• and direct deposition by livestock.
• Develop algorithms and emission factors for use in Tier 2 national GHG inventory

methodologies for developed and developing countries.

• Identify potential mitigation options

Partners

• New Zealand
• UK
• Ireland
• Denmark
• France
• Germany
• Netherlands
• Chile
• Australia

Data also sourced from Argentina, Brazil, Canada, China, Colombia, Italy, Kenya, Nicaragua, Norway, Sweden, Switzerland, Vietnam, and Zimbabwe.

Key steps of Dataman

Database description

GHG Housing (emission rate) Storage (emission rate or cumulative loss) Field (emission factors) NH3 24 37 1243 N2O 4 53 2133 CH4 5 45

• Country

Housing Storage Field Argentina ✓ ✓ Australia ✓ Brazil ✓ ✓ ✓✓ Canada ✓✓ Chile ✓ China ✓ ✓ ✓ Colombia ✓ Kenya ✓ Denmark ✓✓ France ✓ Germany ✓ Ireland ✓ Italy ✓ Netherlands ✓✓ Nicaragua ✓ Norway ✓ NZ ✓✓ UK ✓✓ Sweden ✓ Switzerland ✓ Vietnam ✓ Zimbabwe ✓

Combines existing databases: ALFAM2 (Denmark), AEDA (UK), N2O DB (NZ) Still to be included: ELFE (France) – ca July 2019

Field Database

Livestock types Manure types Manure treatments Manure application method Dairy Cattle solid manure(FYM) none shall inject Beef cattle slurry covered deep inject. Swine layer manure compacted broadcast Poultry broiler litter forced aeration bandspread Sheep dirty water solid separation trailing shoe dung digestion trailing hose urine composted field acidification barn acidification drying urease inhibitor nitrification inhibitor

Field Database: what does NH3 data look like?

NH3 emission factors (FracGASM) for liquid and slurry

Field Database: what does N2O data look like?

N2O emission factors for slurry (EF1) and dung & urine (EF3)

Drivers of N2O from dairy cattle urine: NZ example

• Rainfall - L/T monthly average
• actual in 30 days following urine
• Volumetric water content (v/v)
• Volumetric air content (v/v)
• Water filled pore space (WFPS, %)
• Relative diffusivity (Dp/Do)
• Temperature
• Soil bulk density
• Soil pH
• Soil clay content
• Soil organic C concentration
• Soil cation exchange capacity (CEC)
• N supply

EF3 P < 0.0001 P < 0.0001 P = 0.031 P = 0.029 R2 = 47%

Next steps

• Continue data collation
• Complete data analysis using available data
• Initiate potential improvements to national inventories

using case studies (NZ, Chile) ➢ Looking to add more case study countries.

• Investigate a possible DataMan extension project

➢ Happy to include more countries, more data.

Funding: ➢ NZ Govt via Global Research Alliance (NZ and Chilean scientists) ➢ UK Govt via Defra (UK scientists) ➢ In-kind support from other European institutes (INRA, ATB, Aarhus University, Teagasc, WUR) Researchers for data!!

Acknowledgements