GHG emission projections

from the agricultural sector in Denmark

Steen Gyldenkærne (Agronomist, PhD)

National Environmental Research Institute

Denmark

Workshop on emission projections: Bonn, Germany 6-8 September 2004

Introduction

• Introduction to Danish agriculture• The emission inventories

- Agriculture

- LULUC(F)

• State of the art in the projections- Regression models/macro economic models

• Lessons learned

Source: O. Hertel, NERI (2001)

NH3 and NH4 emission in Europe

• 62% of the land is under plough• Pig production: 85 % is exported• Dairy products: 60% is exported• High leaching rates of nitrogen to coastal waters

- higher than the IPCC default factor

• Deforestation is banned• Very regulated agriculture

- maximum N application rates are 90% of economical optimum- demands for high N-efficiency in manure (50-75%)

• Restablisment of wetlands and extensification of agricultural areas

Danish Agriculture

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

0

2.500.000

5.000.000

7.500.000

10.000.000

12.500.000

15.000.000

Dairy cow s Suckling cow s

Cattle, totalSow sPigs, total

Cattle and sows Pigs, total

Development in animals

0102030405060708090

100

1970 1980 1990 2000 2002

CattlePig

Number of holdings

minus 8-9% p.a.

Average farmer does not exist- lifestyle has disappeared

Data for Agriculture

Statistics Denmark Danish Agricultural Advisory Centre

Danish Institute of Agricultural Sciences

National Environmental Research Institute

NH3 N2O CH4 CO2

Stable

30 Anim al categories110 Different stable

system s(N and C)

Manure store

slurry/solid(N and C)

ApplicationInjection/

broadspreading/ hosetrailing

(N and C)

B iogas P lants(N and C)

Agricultural soils

Lim ing

Mineral Fertiliser

Peat landW etlands

Mineral Organic

C entra l H usbandryR egis ter

F ie ld B lock M aps

G enera l Agricu ltura lR egis ter

Fertiliser R egis ter

S tatis tics D enm ark

N orm data for feedconsum ptions and

excretions(N, P , K and drym atter)

D I E M A

PM

Agricultural inventory in ONE module

Data sheet for Dairy cows (Poulsen et al. 2001)

8

9

10

11

12

13

14

15

1985 1990 1995 2000 2005 2010 2015 2020

Gg

CO

2-eq

v. y

-1

With measures

Without measures

N2O and CH4 emission

with and without measures

8

9

10

11

12

13

14

15

1985 1990 1995 2000 2005 2010 2015 2020

Gg

CO

2-eq

v. y

-1

With CAP reform

With additional Danish measures

With measures

Witout measures

N2O and CH4 emission

with and without measures

Effect of Land Use, IPCC guidelines Tier 2 -

preliminary results

-2,000

-1,500

-1,000

-0,500

0,000

0,500

1,000

1,500

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

CO

2 e

mis

sio

n o

r s

eq

ue

str

ati

on

, Mio

. to

nn

es

CO

2 p

er

ye

ar

Organic soils

Mineral soils

Total

Projection models?

MacroeconomicSupply-Demand

model

Expert knowlegde / Bottom up / regression

model

High national agricultural and environmental regulations

Competitiveness

Industrial state of the agriculture

Veterinarian Standard

Investments in buildings and machinery

Handling Uncertainty - Monte-Carlo

Handling new technology

• DIEMA is able to handle new technology by adding new variables

- today app. 100 different stable combinations

- 26 combinations of manure application

• Biogas plants are incorporated in a preliminary version

• Important with agricultural knowledge, good net-work and colleagues (informal contacts are invaluable)

• Bottom-up models and expert knowledge gives the most detailed projections and may be preferred on the short term

• Macroeconomic models are a good supplement

• International reporting obligations has to be followed up by allocated financial resources

Lessons learned

Thanks to all my colleagues

Thank you for your attention